[1] M. Born, E. Wolf. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light(2013).

[2] M. Khorasaninejad et al. Metalenses at visible wavelengths: diffraction-limited focusing and subwavelength resolution imaging. Science, 352, 1190(2016).

[3] W. S. Boyle. Information storage devices. U.S. patent(1974).

[4] M. F. Tompsett. Charge transfer imaging devices. U.S. patent(1978).

[5] J. N. Mait, R. Athale, J. van der Gracht. Evolutionary paths in imaging and recent trends. Opt. Express, 11, 2093(2003).

[6] J. García et al. Three-dimensional mapping and range measurement by means of projected speckle patterns. Appl. Opt., 47, 3032(2008).

[7] P. Zhou, J. Zhu, Z. You. 3-D face registration solution with speckle encoding based spatial-temporal logical correlation algorithm. Opt. Express, 27, 21004(2019).

[8] J. L. Posdamer, M. D. Altschuler. Surface measurement by space-encoded projected beam systems. Comput. Graph. Image Process., 18, 1(1982).

[9] M. Takeda, K. Mutoh. Fourier transform profilometry for the automatic measurement of 3-D object shapes. Appl. Opt., 22, 3977(1983).

[10] V. Srinivasan, H. C. Liu, M. Halioua. Automated phase-measuring profilometry of 3-D diffuse objects. Appl. Opt., 23, 3105(1984).

[11] G. Sansoni, M. Carocci, R. Rodella. Three-dimensional vision based on a combination of gray-code and phase-shift light projection: analysis and compensation of the systematic errors. Appl. Opt., 38, 6565(1999).

[12] Y. Wang, S. Zhang. Novel phase-coding method for absolute phase retrieval. Opt. Lett., 37, 2067(2012).

[13] D. Zheng, F. Da. Phase coding method for absolute phase retrieval with a large number of codewords. Opt. Express, 20, 24139(2012).

[14] C. Zhou et al. An improved stair phase encoding method for absolute phase retrieval. Opt. Lasers Eng., 66, 269(2015).

[15] H. Zhou et al. Fast phase-measuring profilometry through composite color-coding method. Opt. Commun., 440, 220(2019).

[16] Y. Chen et al. 3D measurement method based on S-shaped segmental phase encoding. Opt. Laser Technol., 121, 105781(2020).

[17] J. Gui et al. Improved dual-frequency phase-coding fringe projection method for 3D shape measurement. J. Mod. Opt., 69, 210(2022).

[18] R. W. Gerchberg. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35, 237(1972).

[19] R. W. Gerchberg, W. Saxton. Phase determination from image and diffraction plane pictures in electron microscope. Optik, 34, 275(1971).

[20] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758(1982).

[21] M. R. Teague. Deterministic phase retrieval: a Green’s function solution. J. Opt. Soc. Am., 73, 1434(1983).

[22] T. E. Gureyev, A. Roberts, K. A. Nugent. Partially coherent fields, the transport-of-intensity equation, and phase uniqueness. J. Opt. Soc. Am. A, 12, 1942(1995).

[23] T. E. Gureyev, A. Roberts, K. A. Nugent. Phase retrieval with the transport-of-intensity equation: matrix solution with use of Zernike polynomials. J. Opt. Soc. Am. A, 12, 1932(1995).

[24] B. Xue, S. Zheng. Phase retrieval using the transport of intensity equation solved by the FMG–CG method. Optik, 122, 2101(2011).

[25] T. E. Gureyev, K. A. Nugent. Rapid quantitative phase imaging using the transport of intensity equation. Opt. Commun., 133, 339(1997).

[26] D. Paganin, K. A. Nugent. Noninterferometric phase imaging with partially coherent light. Phys. Rev. Lett., 80, 2586(1998).

[27] C. Zuo, Q. Chen, A. Asundi. Boundary-artifact-free phase retrieval with the transport of intensity equation: fast solution with use of discrete cosine transform. Opt. Express, 22, 9220(2014).

[28] C. Roddier, F. Roddier. Wave-front reconstruction from defocused images and the testing of ground-based optical telescopes. J. Opt. Soc. Am. A, 10, 2277(1993).

[29] J. M. Soto, J. A. Rodrigo, T. Alieva. Label-free quantitative 3D tomographic imaging for partially coherent light microscopy. Opt. Express, 25, 15699(2017).

[30] S. W. Wilkins et al. Phase-contrast imaging using polychromatic hard X-rays. Nature, 384, 335(1996).

[31] M. G. L. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc., 198, 82(2000).

[32] M. G. L. Gustafsson et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J., 94, 4957(2008).

[33] L. Shao et al. I5S: wide-field light microscopy with 100-nm-scale resolution in three dimensions. Biophys. J., 94, 4971(2008).

[34] J. D. Manton et al. Concepts for structured illumination microscopy with extended axial resolution through mirrored illumination. Biomed. Opt. Express, 11, 2098(2020).

[35] L. Xu et al. Structured illumination microscopy based on asymmetric three-beam interference. J. Innov. Opt. Health Sci., 14, 2050027(2021).

[36] X. Li et al. Three-dimensional structured illumination microscopy with enhanced axial resolution. Nat. Biotechnol., 41, 1307(2023).

[37] C. B. Müller, J. Enderlein. Image scanning microscopy. Phys. Rev. Lett., 104, 198101(2010).

[38] A. G. York et al. Resolution doubling in live, multicellular organisms via multifocal structured illumination microscopy. Nat. Methods, 9, 749(2012).

[39] A. G. York et al. Instant super-resolution imaging in live cells and embryos via analog image processing. Nat. Methods, 10, 1122(2013).

[40] Ø. I. Helle et al. Structured illumination microscopy using a photonic chip. Nat. Photonics, 14, 431(2020).

[41] S. W. Hell, J. Wichmann. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett., 19, 780(1994).

[42] G. Vicidomini et al. STED with wavelengths closer to the emission maximum. Opt. Express, 20, 5225(2012).

[43] P. Gao et al. Background suppression in fluorescence nanoscopy with stimulated emission double depletion. Nat. Photonics, 11, 163(2017).

[44] M. J. Rust, M. Bates, X. Zhuang. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods, 3, 793(2006).

[45] M. Bates et al. Multicolor super-resolution imaging with photo-switchable fluorescent probes. Science, 317, 1749(2007).

[46] Z. Zhang et al. Ultrahigh-throughput single-molecule spectroscopy and spectrally resolved super-resolution microscopy. Nat. Methods, 12, 935(2015).

[47] Y. Shechtman et al. Multicolour localization microscopy by point-spread-function engineering. Nat. Photonics, 10, 590(2016).

[48] B. Huang et al. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science, 319, 810(2008).

[49] B. Huang et al. Whole-cell 3D STORM reveals interactions between cellular structures with nanometer-scale resolution. Nat. Methods, 5, 1047(2008).

[50] M. F. Juette et al. Three-dimensional sub–100 nm resolution fluorescence microscopy of thick samples. Nat. Methods, 5, 527(2008).

[51] S. R. P. Pavani et al. Three-dimensional, single-molecule fluorescence imaging beyond the diffraction limit by using a double-helix point spread function. Proc. Nat. Acad. Sci., 106, 2995(2009).

[52] K. Xu, H. P. Babcock, X. Zhuang. Dual-objective STORM reveals three-dimensional filament organization in the actin cytoskeleton. Nat. Methods, 9, 185(2012).

[53] S. Fu et al. Field-dependent deep learning enables high-throughput whole-cell 3D super- resolution imaging. Nat. Methods, 20, 459(2023).

[54] C. Wagner, W. Osten, S. Seebacher. Direct shape measurement by digital wavefront reconstruction and multi-wavelength contouring. Opt. Eng., 39, 79(2000).

[55] Y. Hayasaki et al. Phase-shifting digital holography using two low-coherence light sources with different wavelength. Proc. SPIE, 6027, 60274V(2006).

[56] F. Willomitzer et al. Synthetic wavelength holography: an extension of Gabor’s holographic principle to imaging with scattered wavefronts(2019).

[57] F. Willomitzer et al. Fast non-line-of-sight imaging with high-resolution and wide field of view using synthetic wavelength holography. Nat. Commun., 12, 6647(2021).

[58] J. M. Rodenburg, H. M. L. Faulkner. A phase retrieval algorithm for shifting illumination. Appl. Phys. Lett., 85, 4795(2004).

[59] Y. Zhou et al. Single-shot lensless imaging via simultaneous multi-angle LED illumination. Opt. Express, 26, 21418(2018).

[60] C. Lu et al. Mask-modulated lensless imaging via translated structured illumination. Opt. Express, 29, 12491(2021).

[61] J. Lan et al. Resolution-enhanced ptychographic modulation imaging via divergent illumination. IEEE Photonics J., 16, 1(2024).

[62] G. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 7, 739(2013).

[63] L. Bian et al. Content adaptive illumination for Fourier ptychography. Opt. Lett., 39, 6648(2014).

[64] Y. Zhang et al. Self-learning based Fourier ptychographic microscopy. Opt. Express, 23, 18471(2015).

[65] S. Li et al. Predictive searching algorithm for Fourier ptychography. J. Opt., 19, 125605(2017).

[66] L. Tian et al. Multiplexed coded illumination for Fourier ptychography with an LED array microscope. Biomed. Opt. Express, 5, 2376(2014).

[67] S. Dong et al. Spectral multiplexing and coherent-state decomposition in Fourier ptychographic imaging. Biomed. Opt. Express, 5, 1757(2014).

[68] J. Sun et al. Single-shot quantitative phase microscopy based on color-multiplexed Fourier ptychography. Opt. Lett., 43, 3365(2018).

[69] S. Sen et al. Fourier ptychographic microscopy using an infrared-emitting hemispherical digital condenser. Appl. Opt., 55, 6421(2016).

[70] A. Pan et al. Subwavelength resolution Fourier ptychography with hemispherical digital condensers. Opt. Express, 26, 23119(2018).

[71] C. Kuang et al. Digital micromirror device-based laser-illumination Fourier ptychographic microscopy. Opt. Express, 23, 26999(2015).

[72] J. Chung et al. Wide-field Fourier ptychographic microscopy using laser illumination source. Biomed. Opt. Express, 7, 4787(2016).

[73] X. Ou et al. Quantitative phase imaging via Fourier ptychographic microscopy. Opt. Lett., 38, 4845(2013).

[74] J. Busck, H. Heiselberg. Gated viewing and high-accuracy three-dimensional laser radar. Appl. Opt., 43, 4705(2004).

[75] P. Andersson. Long-range three-dimensional imaging using range-gated laser radar images. Opt. Eng., 45, 034301(2006).

[76] M. Laurenzis, F. Christnacher, D. Monnin. Long-range three-dimensional active imaging with superresolution depth mapping. Opt. Lett., 32, 3146(2007).

[77] Z. Xiuda, Y. Huimin, J. Yanbing. Pulse-shape-free method for long-range three-dimensional active imaging with high linear accuracy. Opt. Lett., 33, 1219(2008).

[78] Z. Chen et al. Polarization-modulated three-dimensional imaging using a large-aperture electro-optic modulator. Appl. Opt., 57, 7750(2018).

[79] R. Liu, X. Tian, S. Li. Polarisation-modulated photon-counting 3D imaging based on a negative parabolic pulse model. Opt. Express, 29, 20577(2021).

[80] L. Wang et al. Ballistic 2-D imaging through scattering walls using an ultrafast optical Kerr gate. Science, 253, 769(1991).

[81] M. Laurenzis. Investigation of range-gated imaging in scattering environments. Opt. Eng., 51, 061303(2012).

[82] G. Satat, M. Tancik, R. Raskar. Towards photography through realistic fog(2018).

[83] X. Yin et al. Bayesian reconstruction method for underwater 3D range-gated imaging enhancement. Appl. Opt., 59, 370(2020).

[84] D. Kijima et al. Time-of-flight imaging in fog using multiple time-gated exposures. Opt. Express, 29, 6453(2021).

[85] R. Raska, A. Agrawal, J. Tumblin. Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph., 25, 795(2006).

[86] S. McCloskey, Y. Ding, J. Yu. Design and estimation of coded exposure point spread functions. IEEE Trans. Pattern Anal. Mach. Intell., 34, 2071(2012).

[87] L. He et al. Fast image restoration method based on coded exposure and vibration detection. Opt. Eng., 54, 103107(2015).

[88] H. G. Jeon et al. Generating fluttering patterns with low autocorrelation for coded exposure imaging. Int. J. Comput. Vision, 123, 269(2017).

[89] H. G. Jeon et al. Multi-image deblurring using complementary sets of fluttering patterns. IEEE Trans. Image Process, 26, 2311(2017).

[90] D. Huang et al. Optical coherence tomography. Science, 254, 1178(1991).

[91] A. F. Fercher et al. Measurement of intraocular distances by backscattering spectral interferometry. Opt. Commun., 117, 43(1995).

[92] S. R. Chinn, E. A. Swanson, J. G. Fujimoto. Optical coherence tomography using a frequency-tunable optical source. Opt. Lett., 22, 340(1997).

[93] B. Povazay et al. Submicrometer axial resolution optical coherence tomography. Opt. Lett., 27, 1800(2002).

[94] L. Liu et al. Imaging the subcellular structure of human coronary atherosclerosis using micro–optical coherence tomography. Nat. Med., 17, 1010(2011).

[95] D. Cui et al. Dual spectrometer system with spectral compounding for 1-μm optical coherence tomography in vivo. Opt. Lett., 39, 6727(2014).

[96] S. Fuchs et al. Optical coherence tomography with nanoscale axial resolution using a laser-driven high-harmonic source. Optica, 4, 903(2017).

[97] N. M. Israelsen et al. Real-time high-resolution mid-infrared optical coherence tomography. Light Sci. Appl., 8, 11(2019).

[98] J. Jerwick et al. Wide-field ophthalmic space-division multiplexing optical coherence tomography. Photonics Res., 8, 539(2020).

[99] T. S. Ralston et al. Real-time interferometric synthetic aperture microscopy. Opt. Express, 16, 2555(2008).

[100] C. Blatter et al. Extended focus high-speed swept source OCT with self-reconstructive illumination. Opt. Express, 19, 12141(2011).

[101] K. C. Zhou et al. Optical coherence refraction tomography. Nat. Photonics, 13, 794(2019).

[102] M. Pahlevaninezhad et al. Metasurface-based bijective illumination collection imaging provides high-resolution tomography in three dimensions. Nat. Photonics, 16, 203(2022).

[103] M. Wojtkowski. High-speed optical coherence tomography: basics and applications. Appl. Opt., 49, D30(2010).

[104] D. Gabor. A new microscopic principle. Nature, 161, 777(1948).

[105] B. Kemper et al. Characterisation of light emitting diodes (LEDs) for application in digital holographic microscopy for inspection of micro and nanostructured surfaces. Opt. Lasers Eng., 46, 499(2008).

[106] Y. Choi et al. Full-field and single-shot quantitative phase microscopy using dynamic speckle illumination. Opt. Lett., 36, 2465(2011).

[107] J. Cho et al. Dual-wavelength off-axis digital holography using a single light-emitting diode. Opt. Express, 26, 2123(2018).

[108] P. Mann et al. White light interference microscopy with color fringe analysis for quantitative phase imaging and 3-D step height measurement, JW2A.13(2020).

[109] W. Osten et al. Recent advances in digital holography. Appl. Opt., 53, G44(2014).

[110] J. Geng. Structured-light 3D surface imaging: a tutorial. Adv. Opt. Photonics, 3, 128(2011).

[111] M. Saxena, G. Eluru, S. S. Gorthi. Structured illumination microscopy. Adv. Opt. Photonics, 7, 241(2015).

[112] K. Samanta, J. Joseph. An overview of structured illumination microscopy: recent advances and perspectives. J. Opt., 23, 123002(2021).

[113] N. Abramson. Light-in-flight recording by holography. Opt. Lett., 3, 121(1978).

[114] T. Kubota et al. Moving picture recording and observation of three-dimensional image of femtosecond light pulse propagation. Opt. Express, 15, 14348(2007).

[115] K. Goda, K. K. Tsia, B. Jalali. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature, 458, 1145(2009).

[116] A. Kirmani et al. First-photon imaging. Science, 343, 58(2014).

[117] Z. P. Li et al. Super-resolution single-photon imaging at 8.2 kilometers. Opt. Express, 28, 4076(2020).

[118] Z.-P. Li et al. Single-photon computational 3D imaging at 45 km. Photonics Res., 8, 1532(2020).

[119] Z.-P. Li et al. Single-photon imaging over 200 km. Optica, 8, 344(2021).

[120] R. Kumar, B. K. Kaushik, R. Balasubramanian. FPGA implementation of image dehazing algorithm for real time applications. Proc. SPIE, 10396, 1039633(2017).

[121] K. He, J. Sun, X. Tang. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell., 33, 2341(2010).

[122] G. Bi et al. Image dehazing based on accurate estimation of transmission in the atmospheric scattering model. IEEE Photonics J., 9, 7802918(2017).

[123] M. Zhu, B. He, Q. Wu. Single image dehazing based on dark channel prior and energy minimization. IEEE Signal Process. Lett., 25, 174(2018).

[124] S. E. Kim, T. H. Park, I. K. Eom. Fast single image dehazing using saturation based transmission map estimation. IEEE Trans. Image Process., 29, 1985(2020).

[125] Z. Lu, B. Long, S. Yang. Saturation based iterative approach for single image dehazing. IEEE Signal Process Lett., 27, 665(2020).

[126] D. A. Cameron, E. N. Pugh. Double cones as a basis for a new type of polarization vision in vertebrates. Nature, 353, 161(1991).

[127] J. S. Tyo et al. Target detection in optically scattering media by polarization-difference imaging. Appl. Opt., 35, 1855(1996).

[128] J. S. Tyo, E. N. Pugh, N. Engheta. Colorimetric representations for use with polarization-difference imaging of objects in scattering media. J. Opt. Soc. Am. A, 15, 367(1998).

[129] J. Guan, J. Zhu. Target detection in turbid medium using polarization-based range-gated technology. Opt. Express, 21, 14152(2013).

[130] K. M. Yemelyanov et al. Adaptive algorithms for two-channel polarization sensing under various polarization statistics with nonuniform distributions. Appl. Opt., 45, 5504(2006).

[131] E. J. McCartney. Optics of the Atmosphere: Scattering by Molecules and Particles(1975).

[132] S. G. Narasimhan, S. K. Nayar. Vision and the atmosphere. Int. J. Comput. Vis., 48, 233(2002).

[133] S. G. Narasimhan, S. K. Nayar. Contrast restoration of the weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell., 25, 713(2003).

[134] Y. Y. Schechner, S. G. Narasimhan, S. K. Nayar. Instant dehazing of images using Polarization(2001).

[135] Y. Y. Schechner, S. G. Narasimhan, S. K. Naya. Polarization based vision through haze. Appl. Opt., 42, 511(2003).

[136] Y. Y. Schechner, N. Karpel. Recovery of underwater visibility and structure by polarization analysis. IEEE J. Ocean. Eng., 30, 570(2006).

[137] F. Liu et al. Deeply seeing through highly turbid water by active polarization imaging. Opt. Lett., 43, 4903(2018).

[138] H. Tian et al. Rapid underwater target enhancement method based on polarimetric imaging. Opt. Laser Technol., 108, 515(2018).

[139] J. Guan, W. Ren, Y. Cheng. Stokes vector based interpolation method to improve the efficiency of bio-inspired polarization-difference imaging in turbid media. J. Phys. D. Appl. Phys., 51, 145402(2018).

[140] Y. Wei et al. Enhancement of underwater vision by fully exploiting the polarization information from the Stokes vector. Opt. Express, 29, 22275(2021).

[141] H. Jin et al. Polarimetric calculation method of global pixel for underwater image restoration. IEEE Photonics J., 13, 6800315(2021).

[142] S. P. Morgan et al. Rotating orthogonal polarization imaging. Opt. Lett., 33, 1503(2008).

[143] H. Wang et al. Polarization differential imaging in turbid water via Mueller matrix and illumination modulation. Opt. Commun., 499, 127274(2021).

[144] F. Liu et al. Depolarization index from Mueller matrix descatters imaging in turbid water. Chin. Opt. Lett., 20, 022601(2022).

[145] E. Edrei, G. Scarcelli. Memory-effect based deconvolution microscopy for super-resolution imaging through scattering media. Sci. Rep., 6, 33558(2016).

[146] X. Xie et al. Extended depth-resolved imaging through a thin scattering medium with PSF manipulation. Sci. Rep., 8, 4585(2018).

[147] D. Wang et al. Non-invasive super-resolution imaging through dynamic scattering media. Nat. Commun., 12, 3150(2021).

[148] L. Zhu et al. Large field-of-view non-invasive imaging through scattering layers using fluctuating random illumination. Nat. Commun., 13, 1447(2022).

[149] J. Bertolotti et al. Non-invasive imaging through opaque scattering layers. Nature, 491, 232(2012).

[150] O. Katz et al. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics, 8, 784(2014).

[151] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758(1982).

[152] H.-Y. Liu et al. 3D imaging in volumetric scattering media using phase-space measurements. Opt. Express, 23, 14461(2015).

[153] D. Tang et al. Single-shot large field of view imaging with scattering media by spatial demultiplexing. Appl. Opt., 57, 7533(2018).

[154] W. Li et al. Multitarget imaging through scattering media beyond the 3D optical memory effect. Opt. Lett., 45, 2692(2020).

[155] I. Freund, M. Rosenbluh, S. Feng. Memory effects in propagation of optical waves through disordered media. Phys. Rev. Lett., 61, 2328(1988).

[156] I. M. Vellekoop, A. P. Mosk. Focusing coherent light through opaque strongly scattering media. Opt. Lett., 32, 2309(2007).

[157] O. Katz et al. Focusing and compression of ultrashort pulses through scattering media. Nat. Photonics, 5, 372(2011).

[158] O. Katz, E. Small, Y. Silberberg. Looking around corners and through thin turbid layers in real time with scattered incoherent light. Nat. Photonics, 6, 549(2012).

[159] A. P. Mosk et al. Controlling waves in space and time for imaging and focusing in complex media. Nat. Photonics, 6, 283(2012).

[160] I. M. Vellekoop, A. Lagendijk, A. P. Mosk. Exploiting disorder for perfect focusing. Nat. Photonics, 4, 320(2010).

[161] S. M. Popoff et al. Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media. Phys. Rev. Lett., 104, 10060(2010).

[162] Y. Choi et al. Overcoming the diffraction limit using multiple light scattering in a highly disordered medium. Phys. Rev. Lett., 107, 023902(2011).

[163] A. Velten et al. Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging. Nat. Commun., 3, 745(2012).

[164] M. Buttafava et al. Non-line-of-sight imaging using a time-gated single photon avalanche diode. Opt. Express, 23, 20997(2015).

[165] F. Heide et al. Diffuse mirrors: 3D reconstruction from diffuse indirect illumination using inexpensive time-of-flight sensors, 3222(2014).

[166] M. Batarseh et al. Passive sensing around the corner using spatial coherence. Nat. Commun., 9, 3629(2018).

[167] A. Beckus, A. Tamasan, G. K. Atia. Multi-modal non-line-of-sight passive imaging. IEEE Trans. Image Process., 28, 3372(2019).

[168] K. L. Bouman et al. Turning corners into cameras: principles and methods, 2289(2017).

[169] M. Baradad et al. Inferring light fields from shadows, 6267(2018).

[170] C. Saunders et al. Computational periscopy with an ordinary digital camera. Nature, 565, 472(2019).

[171] A. B. Yedidia et al. Using unknown occluders to recover hidden scenes, 12223(2019).

[172] B. Hassan. Polarization-Informed Non-Line-of-Sight Imaging on Diffuse surfaces(2019).

[173] K. Tanaka, Y. Mukaigawa, A. Kadambi. Polarized non-line-of sight imaging, 2133(2020).

[174] T. Maeda et al. Thermal non-line-of-sight imaging, 1(2019).

[175] S. Divitt, D. F. Gardner, A. T. Watnik. Imaging around corners in the mid-infrared using speckle correlations. Opt. Express, 28, 11051(2020).

[176] C. Wu et al. Non–line-of-sight imaging over 1.43 km. Proc. Natl. Acad. Sci., 118, e2024468118(2021).

[177] H.-H. Hsiao et al. Integrated resonant unit of metasurfaces for broadband efficiency and phase manipulation. Adv. Opt. Mater., 6, 1800031(2018).

[178] M. Khorasaninejad, F. Capasso. Broadband multifunctional efficient meta-gratings based on dielectric waveguide phase shifters. Nano. Lett., 15, 6709(2015).

[179] C. P. Jisha, S. Nolte, A. Alberucci. Geometric phase in optics: from wavefront manipulation to waveguiding. Laser Photonics Rev., 15, 2100003(2021).

[180] A. Shaltout et al. Photonic spin Hall effect in gap–plasmon metasurfaces for on-chip chiroptical spectroscopy. Optica, 2, 860(2015).

[181] J. Zeng et al. Generating and separating twisted light by gradient–rotation split-ring antenna metasurfaces. Nano. Lett., 16, 3101(2016).

[182] F. Aieta et al. Multiwavelength achromatic metasurfaces by dispersive phase compensation. Science, 347, 1342(2015).

[183] W. T. Chen et al. “A broadband achromatic metalens for focusing and imaging in the visible. Nat. Nanotechnol., 13, 220(2018).

[184] R. J. Lin et al. “Achromatic metalens array for full-colour light-field imaging. Nat. Nanotechnol., 14, 227(2019).

[185] L. Wang et al. Grayscale transparent metasurface holograms. Optica, 3, 1504(2016).

[186] J. Xiong et al. Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces. Optica, 9, 461(2022).

[187] A. Zaidi et al. Metasurface-enabled single-shot and complete Mueller matrix imaging. Nat. Photonics, 18, 704(2024).

[188] J. Kopf et al. Capturing and viewing gigapixel images. ACM Trans. Graph., 26, 93(2007).

[189] L. Fei et al. Design of multi-scale wide area high-resolution computational imaging system based on concentric spherical lens. Acta Phys. Sin., 68, 084201(2019).

[190] Y. Shen et al. Optical design of a distributed zoom concentric multiscale meteorological instrument. Appl. Opt., 57, 5168(2018).

[191] J.-P. Souchon et al. Is there an ideal digital aerial camera?. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences(2006).

[192] D. Ebbets et al. In-flight photometric performance of the 96Mpx focal plane array assembly for NASA’s Kepler exoplanet mission. Proc. SPIE, 8146, 81460H(2011).

[193] D. Ebbets et al. Optical performance of the 100-sq deg field-of-view telescope for NASA’s Kepler exoplanet mission. Proc. SPIE, 8146, 81460G(2011).

[194] B. Leininger et al. Autonomous real-time ground ubiquitous surveillance-imaging system (ARGUS-IS). Proc. SPIE, 6981, 69810H(2008).

[195] H. Afshari et al. A spherical multi-camera system with real-time omnidirectional video acquisition capability. IEEE Trans. Consum. Electron., 58, 1110(2012).

[196] A. Akin et al. “Hemispherical multiple camera system for high resolution omni-directional light field imaging. IEEE J. Emerg. Sel. Top. Circuits Syst., 3, 137(2013).

[197] O. Cogal, Y. Leblebici. An insect eye inspired miniaturized multi-camera system for endoscopic imaging. IEEE Trans. Biomed. Circuits Syst., 11, 212(2016).

[198] S. Xiaopeng et al. Latest progress in computational imaging technology and application. Laser Photonics Rev., 57, 020001(2020).

[199] D. C. Tilotta, R. M. Hammaker, W. G. Fateley. Multiplex advantage in Hadamard transform spectrometry utilizing solid-state encoding masks with uniform, bistable optical transmission defects. Appl. Opt., 26, 4285(1987).

[200] M. F. Duarte et al. Single-pixel imaging via compressive sampling. IEEE Signal Process Mag., 25, 83(2008).

[201] M. Iliadis, L. Spinoulas, A. K. Katsaggelos. Deep fully-connected networks for video compressive sensing. Digit. Signal Process., 72, 9(2018).

[202] E. R. Dowski, W. T. Cathey. Extended depth of field through wave-front coding. Appl. Opt., 34, 1859(1995).

[203] S. S. Sherif, R. Dowski, W. T. Cathey. Extended depth of field in hybrid imaging systems: circular aperture. J. Mod. Opt., 51, 1191(2004).

[204] R. Raskar, A. Agrawal, J. Tumblin. Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph., 25, 795(2006).

[205] M. E. Gehm et al. Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Express, 15, 14013(2007).

[206] H. Arguello et al. Higher-order computational model for coded aperture spectral imaging. Appl. Opt., 52, D12(2013).

[207] G. R. Arce et al. Compressive coded aperture spectral imaging: an introduction. IEEE Signal Process. Mag., 31, 105(2013).

[208] X. Yuan et al. Compressive hyperspectral imaging with side information. IEEE J. Sel. Top. Signal Process., 9, 964(2015).

[209] L. Wang et al. Compressive hyperspectral imaging with complementary RGB measurements(2016).

[210] E. Y. Lam. Computational photography: advances and challenges. Proc. SPIE, 8122, 81220O(2011).

[211] F. Heide et al. High-quality computational imaging through simple lenses. ACM Trans. Graph., 32, 1(2013).

[212] W. Li et al. A computational photography algorithm for quality enhancement of single lens imaging deblurring. Optik, 126, 2788(2015).

[213] W. Li et al. “Computational imaging through chromatic aberration corrected simple lenses. Mod. Opt., 64, 2211(2017).

[214] M. D. Robinson, D. G. Stork. Joint digital-optical design of superresolution multiframe imaging systems. Appl. Opt., 47, B11(2008).

[215] T. Mirani et al. Computational imaging systems: joint design and end-to-end optimality. Appl. Opt., 47, B86(2008).

[216] J. Dowskir, R. H. Cormack, S. D. Sarama. Wavefront coding: jointly optimized optical and digital imaging systems. Proc. SPIE, 4041, 11(2000).

[217] K. Kubala, E. Dowski, W. T. Cathey. Reducing complexity in computational imaging systems. Opt. Express, 11, 2102(2003).

[218] M. D. Robinson, D. G. Stork. Joint design of lens systems and digital image processing. Proc. SPIE, 6342, 63421G(2006).

[219] X. Shao et al. Study on optical swap computational imaging method, 119(2020).

[220] J. Yang et al. Experimental study on imaging and image deconvolution of a diffractive telescope system. Appl. Opt., 58, 9059(2019).

[221] C. A. Metzler et al. Deep optics for single-shot high-dynamic-range imaging, 137(2020).

[222] X. Dun et al. Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging. Optica, 7, 913(2020).

[223] S. Wei et al. Low-cost and simple optical system based on wavefront coding and deep learning. Appl. Opt., 62, 6171(2023).

[224] R. Davies, M. Kasper. Adaptive optics for astronomy. Annu. Rev. Astron. Astrophys., 50, 305(2012).

[225] H. W. Babcock. The possibility of compensating astronomical seeing. Publ. Astron. Soc. Pac., 65, 229(1953).

[226] J. W. Hardy, J. E. Lefebvre, C. L. Koliopoulos. Real-time atmospheric compensation. J. Opt. Soc. Am., 67, 360(1977).

[227] H. Wu et al. Study on beam propagation through a double-adaptive-optics optical system in turbulent atmosphere. Opt. Quantum Electron., 45, 411(2013).

[228] G. Gao et al. The measurement and correction of atmospheric dispersion in 4-meter telescope with adaptive optical system. IEEE Photonics J., 5, 6000106(2023).

[229] O. Azucena et al. Wavefront aberration measurements and corrections through thick tissue using fluorescent microsphere reference beacons. Opt. Express, 18, 17521(2010).

[230] R. Jorand et al. Deep and clear optical imaging of thick inhomogeneous samples. PLoS One, 7, e35795(2012).

[231] J. Liang, D. R. Williams, D. T. Miller. Supernormal vision and high-resolution retinal imaging through adaptive. J. Opt. Soc. Am. A Opt. Image Sci. Vis., 14, 2884(1997).

[232] Q. Yang et al. Closed-loop optical stabilization and digital image registration in adaptive optics scanning light ophthalmoscopy. Biomed. Opt. Express, 5, 3174(2014).

[233] S. Lee et al. High-speed adaptive optics partially confocal ophthalmoscope based on digital micromirror device (DMD). Proc. SPIE, 12360, 1236008(2023).

[234] R. Ng. Digital Light Field Photography(2006).

[235] M. Broxton et al. Wave optics theory and 3-D deconvolution for the light field microscope. Opt. Express, 21, 25418(2013).

[236] L. Kyle. Development of a 3-D Fluid Velocimetry Technique Based on Light Field Imaging(2011).

[237] E. Magdaleno, M. Rodríguez, J. M. Rodríguez-Ramos. An efficient pipeline wavefront phase recovery for the CAFADIS camera for extremely large telescopes. Sensors, 10, 1(2010).

[238] B. Neichel, T. Fusco, J.-M. Conan. Tomographic reconstruction for wide-field adaptive optics systems: Fourier domain analysis and fundamental limitations. J. Opt. Soc. Am. A, 26, 219(2009).

[239] J. M. Rodríguez et al. The CAFADIS camera: a new tomographic wavefront sensor for adaptive optics, 05011(2010).

[240] Z. Lu et al. Phase-space deconvolution for light field microscopy. Opt. Express, 27, 18131(2019).

[241] D. L. Donoho. Compressed sensing. IEEE Trans. Inf. Theory, 52, 1289(2006).

[242] D. Takhar et al. A new compressive imaging camera architecture using optical-domain compression. Proc. SPIE, 6065, 606509(2006).

[243] A. F. Abouraddy et al. Role of entanglement in two-photon imaging. Phys. Rev. Lett., 87, 123602(2001).

[244] A. Gatti et al. Ghost imaging with thermal light: comparing entanglement and classical correlation. Phys. Rev. Lett., 93, 093602(2004).

[245] A. Valencia et al. Two-photon imaging with thermal light. Phys. Rev. Lett., 94, 063601(2005).

[246] J. H. Shapiro. Computational ghost imaging. Phys. Rev. A., 78, 061802(R)(2008).

[247] K. W. Chan, M. N. O’Sullivan, R. W. Boyd. High-order thermal ghost imaging. Opt. Lett., 34, 3343(2009).

[248] S. Liu et al. Hyperspectral ghost imaging camera based on a flat-field grating. Opt. Express, 26, 17705(2018).

[249] M.-J. Sun et al. Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning. Opt. Express, 24, 10476(2016).

[250] K. M. Czajkowski, A. Pastuszczak, R. Kotyński. Real-time single-pixel video imaging with Fourier domain regularization. Opt. Express, 26, 20009(2018).

[251] Y. Zou, X. Pan, E. Y. Sidky. Image reconstruction in regions-of-interest from truncated projections in a reduced fan-beam scan. Phys. Med. Biol., 50, 13(2004).

[252] D. B. Phillips et al. Adaptive foveated single-pixel imaging with dynamic supersampling. Sci. Adv., 3, e1601782(2017).

[253] Z. Ye et al. Secured regions of interest (SROIs) in single-pixel imaging. Sci. Rep., 9, 12782(2019).

[254] B. Zeng et al. Hybrid graphene metasurfaces for high-speed mid-infrared light modulation and single-pixel imaging. Light Sci. Appl., 7, 51(2018).

[255] N. Huynh et al. Single-pixel camera photoacoustic tomography. J. Biomed Opt., 24, 1(2019).

[256] M. J. Sun et al. Single-pixel three-dimensional imaging with time-based depth resolution. Nat. Commun., 7, 12010(2016).

[257] H. Deng et al. Transmissive single-pixel microscopic imaging through scattering media. Sensors, 21, 2721(2021).

[258] R. I. Stantchev et al. Real-time terahertz imaging with a single-pixel detector. Nat. Commun., 11, 2535(2020).

[259] Y.-H. He et al. Single-pixel imaging with neutrons. Sci. Bull., 66, 133(2021).

[260] M. Pelissier, C. Studer. Non-uniform wavelet sampling for RF analog-to-information conversion. IEEE Trans. Circuits Syst. I: Regul. Pap., 65, 471(2017).

[261] D. Gołowicz et al. Fast time-resolved NMR with non-uniform sampling. Prog. Nucl. Magn. Reson. Spectrosc., 116, 40(2020).

[262] Z. Wang et al. Adaptive high-resolution imaging method based on compressive sensing. Sensors, 22, 8848(2022).

[263] A. Gao et al. “Real-time stereo 3D car detection with shape-aware non-uniform sampling. IEEE Trans. Intell. Trans. Syst., 24, 4027(2023).

[264] A. J. J. M. van Breemen et al. Curved digital X-ray detectors. npj Flexible Electron., 4, 22(2020).

[265] L. Gu et al. A biomimetic eye with a hemispherical perovskite nanowire array retina. Nature, 581, 278(2020).

[266] Z. Rao et al. Curvy, shape-adaptive imagers based on printed optoelectronic pixels with a kirigami design. Nat. Electron., 4, 513(2021).

[267] Y. Zhang et al. Advanced biomimetic multispectral curved compound eye camera for aerial multispectral imaging in a large field of view. Biomimetics, 8, 556(2023).

[268] O. Mitrofanov et al. Efficient photoconductive terahertz detector with all-dielectric optical metasurface. APL Photonics, 3, 051703(2018).

[269] T. Siday et al. Terahertz detection with perfectly-absorbing photoconductive metasurface. Nano Lett., 19, 2888(2019).

[270] L. Li et al. Intelligent metasurface imager and recognizer. Light Sci. Appl., 8, 97(2019).

[271] J. Zhou et al. Optical edge detection based on high-efficiency dielectric metasurface. Proc. Natl. Acad. Sci. U.S.A., 116, 11137(2019).

[272] J. Zhou et al. Metasurface enabled quantum edge detection. Sci. Adv., 6, eabc4385(2020).

[273] R. J. Martins et al. Metasurface-enhanced light detection and ranging technology. Nat. Commun., 13, 5724(2022).

[274] I. Tanriover, S. A. Dereshgi, K. Aydin. Metasurface enabled broadband all optical edge detection in visible frequencies. Nat. Commun., 14, 648(2023).

[275] W. H. P. Pernice et al. High-speed and high-efficiency travelling wave single-photon detectors embedded in nanophotonic circuits. Nat. Commun., 3, 132(2012).

[276] J. Chen et al. Short-time velocity identification and coherent-like detection of ultrahigh speed targets. IEEE Trans. Signal Process., 66, 4811(2018).

[277] J. Li et al. A hardware-oriented algorithm for ultra-high-speed object detection. IEEE Sens. J., 19, 3818(2019).

[278] H. Rebecq et al. High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell., 43, 1964(2019).

[279] A. Glover et al. luvHarris: a practical corner detector for event-cameras. IEEE Trans. Pattern Anal. Mach. Intell., 44, 10087(2021).

[280] R. W. Baldwin et al. Time-ordered recent event (TORE) volumes for event cameras. IEEE Trans. Pattern Anal. Mach. Intell., 45, 2519(2022).

[281] Z. Zhou, S. Li, B. Wang. Multi-scale weighted gradient-based fusion for multi-focus images. Inf. Fusion, 20, 60(2014).

[282] Y. Liu, S. Liu, Z. Wang. Multi-focus image fusion with dense SIFT. Inf. Fusion, 23, 139(2014).

[283] H. Zhang et al. MFF-GAN: an unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Inf. Fusion, 66, 40(2021).

[284] J. Li et al. AttentionFGAN: infrared and visible image fusion using attention-based generative adversarial networks. IEEE Trans. Multimedia, 23, 1383(2021).

[285] L. Tang, J. Yuan, J. Ma. Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network. Inf. Fusion, 82, 28(2022).

[286] L. Tang et al. PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Inf. Fusion, 83, 79(2022).

[287] R. Dian, S. Li, X. Kang. Regularizing hyperspectral and multispectral image fusion by CNN denoiser. IEEE Trans. Neural Netw. Learn. Syst., 32, 1124(2021).

[288] Q. Xie et al. MHF-Net: an interpretable deep network for multispectral and hyperspectral image fusion. IEEE Trans. Pattern Anal. Mach. Intell., 44, 1457(2022).

[289] W. Sun et al. MLR-DBPFN: a multi-scale low rank deep back projection fusion network for anti-noise hyperspectral and multispectral image fusion. IEEE Trans. Geosci. Remote Sens., 60, 5522914(2022).

[290] C. Lee, C. Lee, C.-S. Kim. Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans. Image Process., 22, 537(2013).

[291] J. Cai, S. Gu, L. Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans. Image Process., 27, 2049(2018).

[292] S. Agrawal et al. A novel joint histogram equalization based image contrast enhancement. J. King Saud Univ. Comput. Inf. Sci., 34, 117(2022).

[293] K. Xiaodong et al. Single infrared image enhancement using a deep convolutional neural network. Neural Comput., 332, 119(2018).

[294] X. Fu, X. Cao. Underwater image enhancement with global–local networks and compressed-histogram equalization. Signal Process. Image Commun., 86, 115892(2020).

[295] K. G. Lore, A. Akintayo, S. Sarkar. LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognit., 61, 650(2017).

[296] F. Lv, Y. Li, F. Lu. Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int. J. Comput. Vis., 129, 2175(2021).

[297] C. Li, C. Guo, C. C. Loy. Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Trans. Pattern Anal. Mach. Intell., 44, 4225(2022).

[298] Z. Zhao et al. RetinexDIP: a unified deep framework for low-light image enhancement. IEEE Trans. Circuits Syst. Video Technol., 32, 1076(2022).

[299] A. Singh et al. Low-light image enhancement for UAVs with multi-feature fusion deep neural networks. IEEE Trans. Geosci. Remote Sens., 19, 3513305(2022).

[300] X. Li, M. Liu, Q. Ling. Pixel-wise gamma correction mapping for low-light image enhancement. IEEE Trans. Circuits Syst. Video Technol., 34, 681(2024).

[301] S. Yang, D. Zhou. Efficient low-light image enhancement with model parameters scaled down to 0.02M. Int. J. Mach. Learn. Cyber., 15, 1575(2024).

[302] Y. Rivenson et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng., 3, 466(2019).

[303] D. Li et al. Deep learning for virtual histological staining of bright-field microscopic images of unlabeled carotid artery tissue. Mol. Imaging Biol., 22, 1301(2020).

[304] X. Meng, X. Li, X. Wang. A computationally virtual histological staining method to ovarian cancer tissue by deep generative adversarial networks. Comput. Math. Methods Med., 2021, 4244157(2021).

[305] P. E. Debevec, J. M. Malik. Recovering high dynamic range radiance maps from photographs, 369(1997).

[306] T. Jinno, M. Okuda. Multiple exposure fusion for high dynamic range image acquisition. IEEE Trans. Image Process., 21, 358(2012).

[307] D. Han et al. Multi-exposure image fusion via deep perceptual enhancement. Inf. Fusion, 79, 248(2022).

[308] G. Eilertsen et al. HDR image reconstruction from a single exposure using deep CNNs. ACM Trans. Graph., 36, 1(2017).

[309] Y. Niu et al. HDR-GAN: HDR image reconstruction from multi-exposed LDR images with large motions. IEEE Trans. Image Process., 30, 3885(2021).

[310] X. Tan et al. Deep multi-exposure image fusion for dynamic scenes. IEEE Trans. Image Process., 32, 5310(2023).

[311] D. Rajan, S. Chaudhuri. Generalized interpolation and its application in super-resolution imaging. Image Vision Comput., 19, 957(2001).

[312] L. Zhang, X. Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process., 15, 3409(2006).

[313] W. Wu, Z. Liu, X. He. Learning-based super resolution using kernel partial least squares. Image Vision Comput., 29, 394(2011).

[314] L. Wang et al. Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans. Circuits Syst. Video Technol., 23, 1289(2013).

[315] W. Dong et al. Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Trans. Image Process., 22, 1382(2013).

[316] C. Liu, D. Sun. On Bayesian adaptive video super resolution. IEEE Trans. Pattern Anal. Mach. Intell., 36, 346(2014).

[317] Q. Yang et al. Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction. ISA Trans., 82, 163(2018).

[318] C. Dong et al. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell., 38, 29(2016).

[319] R. Lan et al. MADNet: a fast and lightweight network for single-image super resolution. IEEE Trans. Cybern., 51, 1443(2021).

[320] X. Dong et al. Remote sensing image super-resolution using novel dense-sampling networks. IEEE Trans. Geosci. Remote Sens., 59, 1618(2021).

[321] C. Tian et al. Coarse-to-fine CNN for image super-resolution. IEEE Trans. Multimedia, 23, 1489(2021).

[322] S. Anwar, N. Barnes. Densely residual Laplacian super-resolution. IEEE Trans. Pattern Anal. Mach. Intell., 44, 1192(2022).

[323] R. Dong, L. Zhang, H. Fu. RRSGAN: reference-based super-resolution for remote sensing image. IEEE Trans. Geosci. Remote Sens., 60, 5601117(2022).

[324] X. Zhu et al. Lightweight image super-resolution with expectation-maximization attention mechanism. IEEE Trans. Circuits Syst. Video Technol., 32, 1273(2022).

[325] S. Lei, Z. Shi, W. Mo. Transformer-based multistage enhancement for remote sensing image super-resolution. IEEE Trans. Geosci. Remote Sens., 60, 5615611(2022).

[326] X. Zhu et al. Cross view capture for stereo image super-resolution. IEEE Trans. Multimedia, 24, 3074(2022).

[327] F. Wang et al. Far-field super-resolution ghost imaging with a deep neural network constraint. Light Sci. Appl., 11, 1(2022).

[328] K. Park, J. W. Soh, N. I. Cho. A dynamic residual self-attention network for lightweight single image super-resolution. IEEE Trans. Multimedia, 25, 907(2023).

[329] J. E. Harvey et al. Modeling physical optics phenomena by complex ray tracing. Opt. Eng., 54, 035105(2015).

[330] Y. W. Tia et al. Coded exposure imaging for projective motion deblurring. IEEE Computer Society Conference on Computer Vision and Pattern Recognition(2010).

[331] R. Liu et al. Polarisation-modulated photon-counting 3D imaging based on a negative parabolic pulse model. Opt. Express, 29, 20577(2021).

[332] K. Chen et al. A reconfigurable active Huygens’ metalens. Adv. Mater., 29, 1606422(2017).

[333] J. E. Greivenkamp et al. Optical testing using Shack-Hartmann wavefront sensors. Proc. SPIE, 4416, 260(2001).

[334] N. Ji. Adaptive optical fluorescence microscopy. Nat. Methods, 14, 374(2017).

[335] Y. B. Ni et al. Multidimensional light field sensing based on metasurfaces. Chin. J. Lasers, 48, 1918003(2021).

[336] Z. Liu et al. All-fiber high-speed image detection enabled by deep learning. Nat. Commun., 13, 1433(2022).

[337] M. Gehrig et al. Event-based angular velocity regression with spiking networks. IEEE International Conference on Robotics and Automation (ICRA)(2020).