[1] HE P H, YU Y, XU C Y. Image super-resolution reconstruction network based on dynamic pyramid and subspace attention[J]. Computer Science, 49, 210900202(2022).

[2] PARK S C, PARK M K, KANG M G. Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Processing Magazine, 20, 21-36(2003).

[3] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-508(2006).

[4] HOU H, ANDREWS H. Cubic splines for image interpolation and digital filtering[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26, 508-517(1978).

[5] KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153-1160(1981).

[6] BABACAN S D, MOLINA R, KATSAGGELOS A K. Parameter estimation in TV image restoration using variational distribution approximation[J]. IEEE Transactions on Image Processing, 17, 326-339(2008).

[7] PATTI A J, SEZAN M I, TEKALP A M. Robust methods for high-quality stills from interlaced video in the presence of dominant motion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 7, 328-342(1997).

[8] IRANI M, PELEG S. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing, 53, 231-239(1991).

[9] LIN G J, JIANG X G, YANG M Z et al. Face recognition based on superposed collaborative representation based classification[J]. Chinese Journal of Liquid Crystals and Displays, 35, 161-166(2020).

[10] HE L, QI H, ZARETZKI R. Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution[C], 345-352(2013).

[11] XIE B, HUANG A, HUANG H. Colorimage denoising algorithm based on intrinsic image decomposition and sparse representation[J]. Chinese Journal of Liquid Crystals and Displays, 34, 1104-1114(2019).

[12] YANG J C, WRIGHT J, HUANG T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010).

[13] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C], 711-730(2012).

[14] LUO Y, ZHAO Y F, LI J X et al. Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. eLight, 2, 4(2022).

[15] ZUO C, QIAN J M, FENG S J et al. Deep learning in optical metrology: a review[J]. Light: Science & Applications, 11, 39(2022).

[16] SITU G. Deep holography[J]. Light: Advanced Manufacturing, 3, 8(2022).

[17] DONG C, LOY C C, HE K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

[18] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

[19] SHI W Z, CABALLERO J, HUSZÁR F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C], 1874-1883(2016).

[20] HUANG Y W, TANG X, ZHOU B. Image super-resolution reconstruction network with dual attention and structural similarity measure[J]. Chinese Journal of Liquid Crystals and Displays, 37, 367-375(2022).

[21] ZHAO Y Y, SHI S X. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 47, 200007(2020).

[22] GREGOR K, LECUN Y. Learning fast approximations of sparse coding[C], 399-406(2010).

[23] DAUBECHIES I, DEFRISE M, DE MOL C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 57, 1413-1457(2004).

[24] LIU D, WANG Z W, WEN B H et al. Robust single image super-resolution via deep networks with sparse prior[J]. IEEE Transactions on Image Processing, 25, 3194-3207(2016).

[25] SRETER H, GIRYES R. Learned convolutional sparse coding[C], 2191-2195(2018).

[26] CRESWELL A, WHITE T, DUMOULIN V et al. Generative adversarial networks: an overview[J]. IEEE Signal Processing Magazine, 35, 53-65(2018).

[27] LEDIG C, THEIS L, HUSZÁR F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

[28] WANG X T, YU K, WU S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[C], 63-79(2019).

[29] TOŠIĆ I, FROSSARD P. Dictionary learning[J]. IEEE Signal Processing Magazine, 28, 27-38(2011).

[30] CAI T T, WANG L. Orthogonal matching pursuit for sparse signal recovery[J]. IEEE Transactions on Information Theory, 57, 4680-4688(2011).

[31] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 54, 4311-4322(2006).

[32] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C], 1-14(2015).

[33] ZHANG R, ISOLA P, EFROS A A et al. The unreasonable effectiveness of deep features as a perceptual metric[C], 586-595(2018).