[1] A. Khan, A. D. Vibhute, S. Mali. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecol. Inform., 69, 101678(2022).

[2] A. S. Sahadevan. Extraction of spatial-spectral homogeneous patches and fractional abundances for field-scale agriculture monitoring using airborne hyperspectral images. Comput. Electron. Agr., 188, 106325(2021).

[3] L. Guo, X. Sun, P. Fu. Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas. Geoderma, 398, 115118(2021).

[4] Y. Gu, T. Liu, G. Gao. Multimodal hyperspectral remote sensing: an overview and perspective. Sci. China Inform. Sci., 64, 121301(2021).

[5] G. Vivone. Multispectral and hyperspectral image fusion in remote sensing: a survey. Inform. Fusion, 89, 405-417(2023).

[6] Y. Zhong, X. Wang, S. Wang. Advances in spaceborne hyperspectral remote sensing in China. Geo-Spat. Inf. Sci., 24, 95-120(2021).

[7] K. Liu, S. Lin, S. Zhu. Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells. Biomed. Opt. Express, 12, 173-180(2021).

[8] L. A. Courtenay, D. González-Aguilera, S. Lagüela. Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis. Biomed. Opt. Express, 12, 5107-5127(2021).

[9] P. N. Hedde, R. Cinco, L. Malacrida. Phasor-based hyperspectral snapshot microscopy allows fast imaging of live, three-dimensional tissues for biomedical applications. Commun. Biol., 4, 721(2021).

[10] T. Morimoto, S. Kishigami, J. M. Linhares. Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments. Opt. Express, 27, 32277-32293(2019).

[11] G. Lassalle. Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: recommendations and guidelines based on a meta-review. Sci. Total Environ., 788, 147758(2021).

[12] C. Liu, C. Xing, Q. Hu. Stereoscopic hyperspectral remote sensing of the atmospheric environment: innovation and prospects. Earth-Sci. Rev., 226, 103958(2022).

[13] K. C. Tiwari, M. K. Arora, D. Singh. An assessment of independent component analysis for detection of military targets from hyperspectral images. Int. J. Appl. Earth Obs., 13, 730-740(2011).

[14] A. Hossain. Spectral simulation and method design of camouflage textiles for concealment of hyperspectral imaging in UV-Vis-IR against multidimensional combat background. J. Text. I, 114, 331-342(2023).

[15] B. Chen, L. Liu, Z. Zou. Target detection in hyperspectral remote sensing image: current status and challenges. Remote Sens., 15, 3223(2023).

[16] G. Özdoğan, X. Lin, D. Sun. Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: recent application developments. Trends Food Sci. Tech., 111, 151-165(2021).

[17] R. Sarić, V. D. Nguyen, T. Burge. Applications of hyperspectral imaging in plant phenotyping. Trends Plant Sci., 27, 301-315(2022).

[18] W. Peng, G. Beggio, A. Pivato. Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: a review. Renew. Sust. Energ. Rev., 165, 112608(2022).

[19] N. Pahlevan, B. Smith, C. Binding. Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters. Remote Sens. Environ., 253, 112200(2021).

[20] M. J. Khan, H. S. Khan, A. Yousaf. Modern trends in hyperspectral image analysis: a review. IEEE Access, 6, 14118-14129(2018).

[21] G. Jaiswal, A. Sharma, S. K. Yadav. Critical insights into modern hyperspectral image applications through deep learning. WiRes. Data Min. Knowl., 11, e1426(2021).

[22] E. J. Candès, J. Romberg, T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52, 489-509(2006).

[23] E. J. Candes, T. Tao. Decoding by linear programming. IEEE Inform. Theory, 51, 4203-4215(2005).

[24] Z. Rahman, D. J. Brady, S. E. Reichenbach. Compressive imaging spectrometers using coded apertures. Proc. SPIE, 6246, 62460A(2006).

[25] M. E. Gehm, R. John, D. J. Brady. Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Express, 15, 14013-14027(2007).

[26] D. P. Casasent, A. A. Wagadarikar, T. Clark. Single disperser design for compressive, single-snapshot spectral imaging. Proc. SPIE, 6714, 67140A(2007).

[27] C. V. Correa, H. Arguello, G. R. Arce. Snapshot colored compressive spectral imager. J. Opt. Soc. Am. A, 32, 1754-1763(2015).

[28] C.-H. Lin, S.-H. Huang, T.-H. Lin. Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory. Nat. Commun., 14, 6979(2023).

[29] W. Zhang, H. Song, X. He. Deeply learned broadband encoding stochastic hyperspectral imaging. Light Sci. Appl., 10, 108(2021).

[30] B. Seong, W. Kim, Y. Kim. E2E-BPF microscope: extended depth-of-field microscopy using learning-based implementation of binary phase filter and image deconvolution. Light Sci. Appl., 12, 269(2023).

[31] W. Li, P. He, D. Lei. Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus. Nat. Commun., 14, 5107(2023).

[32] E. R. Dowski, W. T. Cathey. Extended depth of field through wave-front coding. Appl. Opt., 34, 1859-1866(1995).

[33] D. Hong, K. Park, H. Cho. Flexible depth-of-field imaging system using a spatial light modulator. Appl. Opt., 46, 8591-8599(2007).

[34] A. Castro, J. Ojeda-Castañeda. Asymmetric phase masks for extended depth of field. Appl. Opt., 43, 3474-3479(2004).

[35] B. Forster, D. Van De Ville, J. Berent. Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images. Microsc. Res. Techniq., 65, 33-42(2004).

[36] W. Chi, N. George. Electronic imaging using a logarithmic asphere. Opt. Lett., 26, 875-877(2001).

[37] J. Ojeda-Castaneda, E. Tepichin, A. Diaz. Arbitrarily high focal depth with a quasioptimum real and positive transmittance apodizer. Appl. Opt., 28, 2666-2670(1989).

[38] A. Flores, M. R. Wang, J. J. Yang. Achromatic hybrid refractive-diffractive lens with extended depth of focus. Appl. Opt., 43, 5618-5630(2004).

[39] S. S. Sherif, W. T. Cathey, E. R. Dowski. Phase plate to extend the depth of field of incoherent hybrid imaging systems. Appl. Opt., 43, 2709-2721(2004).

[40] S. Elmalem, R. Giryes, E. Marom. Learned phase coded aperture for the benefit of depth of field extension. Opt. Express, 26, 15316-15331(2018).

[41] J. R. C. S. A. V. S. Neto, T. Nakamura, Y. Makihara. Extended depth-of-field lensless imaging using an optimized radial mask. IEEE Trans. Comput. Imaging, 9, 857-868(2023).

[42] S. Pinilla, J. E. Fröch, S. R. Miri Rostami. Miniature color camera via flat hybrid meta-optics. Sci. Adv., 9, 7297(2023).

[43] W. Hadibrata, H. Wei, S. Krishnaswamy. Inverse design and 3D printing of a metalens on an optical fiber tip for direct laser lithography. Nano Lett., 21, 2422-2428(2021).

[44] S. L. Oscurato, F. Reda, M. Salvatore. Shapeshifting diffractive optical devices. Laser Photonics Rev., 16, 2100514(2022).

[45] Z. Hu, T. Jiang, Z. Tian. Broad-bandwidth micro-diffractive optical elements. Laser Photonics Rev., 16, 2100537(2022).

[46] W. A. Britton, Y. Chen, F. Sgrignuoli. Compact dual-band multi-focal diffractive lenses. Laser Photonics Rev., 15, 2000207(2021).

[47] J. Tan, G. Wang, Y. Li. Femtosecond laser fabrication of refractive/diffractive micro-optical components on hard brittle materials. Laser Photonics Rev., 17, 2200692(2023).

[48] R. Orange kedem, N. Opatovski, D. Xiao. Near index matching enables solid diffractive optical element fabrication via additive manufacturing. Light Sci. Appl., 12, 222(2023).

[49] Z. Luo, Y. Li, J. Semmen. Achromatic diffractive liquid-crystal optics for virtual reality displays. Light Sci. Appl., 12, 230(2023).

[50] D. S. Jeon, S. Baek, S. Yi. Compact snapshot hyperspectral imaging with diffracted rotation. ACM Trans. Graph., 38, 117(2019).

[51] S.-H. Baek, H. Ikoma, D. S. Jeon. Single-shot hyperspectral-depth imaging with learned diffractive optics. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2651-2660(2021).

[52] J. Bacca, E. Martinez, H. Arguello. Computational spectral imaging: a contemporary overview. J. Opt. Soc. Am. A, 40, C115-C125(2023).

[53] L. Huang, J. Whitehead, S. Colburn. Design and analysis of extended depth of focus metalenses for achromatic computational imaging. Photonics Res., 8, 1613-1623(2020).

[54] L. Huang, Z. Han, A. Wirth-Singh. Broadband thermal imaging using meta-optics. Nat. Commun., 15, 1662(2024).

[55] E. Bayati, R. Pestourie, S. Colburn. Inverse designed metalenses with extended depth of focus. ACS Photonics, 7, 873-878(2020).

[56] I. A. A. Appak, E. Sahin, C. Guillemot. Learning flat optics for extended depth of field microscopy imaging. Nanophotonics, 12, 3623-3632(2023).

[57] Y. Zhang, X. Song, J. Xie. Large depth-of-field ultra-compact microscope by progressive optimization and deep learning. Nat. Commun., 14, 4118(2023).

[58] J. Suszek, M. Makowski, A. Kolodziejczyk. Extended depth of focus for high-end machine vision lenses by annular achromatic add-on diffractive elements. Opt. Laser Eng., 162, 107445(2023).

[59] S. Banerji, M. Meem, A. Majumder. Extreme-depth-of-focus imaging with a flat lens. Optica, 7, 214-217(2020).

[60] X. Dun, H. Ikoma, G. Wetzstein. Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging. Optica, 7, 913-922(2020).

[61] Q. Q. Zhang, J. G. Wang, M. W. Wang. A modified fractal zone plate with extended depth of focus in spectral domain optical coherence tomography. J. Opt., 13, 055301(2011).

[62] E. Vargas, H. Rueda-Chacón, H. Arguello. Learning time-multiplexed phase-coded apertures for snapshot spectral-depth imaging. Opt. Express, 31, 39796-39810(2023).

[63] Z. Zhang, L. Xie, J. Zhang. Enhancing photon throughput of miniaturized passive depth-detection cameras via broadband dispersion-engineered metalenses. ACS Photonics, 10, 3789-3796(2023).

[64] E. Sahin, U. Akpinar, A. Kim. Learning extended depth of field hyperspectral imaging. IEEE International Conference on Image Processing, 1850-1854(2023).

[65] A. Fontbonne, H. Sauer, F. Goudail. End-to-end optimization of optical systems with extended depth of field under wide spectrum illumination. Appl. Opt., 61, 5358-5367(2022).

[66] T. Kou, Q. Zhang, C. Zhang. Large depth-of-field computational imaging with multi-spectral and dual-aperture optics. Opt. Express, 30, 32540-32564(2022).

[67] https://opticapublishing.figshare.com/s/b2a2f58dca71d5d545b3

[68] B. Manifold, S. Men, R. Hu. A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. Nat. Mach. Intell., 3, 306-315(2021).

[69] B. Arad, R. Timofte, O. Ben-Shahar. NTIRE 2020 challenge on spectral reconstruction from an RGB image. Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 446-447(2020).

[70] Y. Wang, M. Gu. The concept of spectral accuracy for MS. Anal. Chem., 82, 7055-7062(2010).