[1] Jiang Chengming, Song Jinhui. An ultrahigh-resolution digital image sensor with pixel size of 50 nm by vertical nanorod arrays[J]. Advanced Materials, 27, 4454-4460(2015).

[2] Zhang Xiu, Zhou Wei. Image super-resolution reconstruction based on convolution sparse self-encoding[J]. Infrared and Laser Engineering, 48, 0126005(2019).

[3] Xi Zhihong, Hou Caiyan, Yuan Kunpeng, et al. Accelerated image super-resolution reconstruction based on deep residual network[J]. Acta Optica Sinica, 89-98(2019).

[4] [4] Siu WanChi, Hung KwokWai. Review of image interpolation superresolution[C]AsiaPacific Signal Infmation Processing Association Annual Summit Conference, 2012.

[5] Fernandez-Beltran R, Latorre-Carmona P, Pla F. Single-frame super-resolution in remote sensing: A practical overview[J]. International Journal of Remote Sensing, 38, 314-354(2017).

[6] [6] Bertero M, Boccacci P. Introduction to Inverse Problems Imaging[M]. Bristol: Institute of Physics Publishing, 1998: 18.

[7] [7] Zhao X Q, Jia Y X. An adaptive regularization image superresolution reconstruction algithm[C]2014 33rd Chinese Control Conference (CCC). IEEE, 2014.

[8] Cai Q, Liu Y D, Cao J, et al. A watershed image segmentation algorithm based on self-adaptive marking and interregional affinity propagation clustering[J]. Acta Electronica Sinica, 45, 1911-1918(2017).

[9] Jing L, Liu S, Zhihong L, et al. An image reconstruction algorithm based on the extended Tikhonov regularization method for electrical capacitance tomography[J]. Measurement, 42, 368-376(2009).

[10] Gou Shuiping, Liu Shuzhen, Wu Yaosheng, et al. Image super-resolution based on the pairwise dictionary selected learning and improved bilateral regularization[J]. IET Image Processing, 10, 101-112(2016).

[11] Lee E S, Kang M G. Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration[J]. IEEE Transactions on Image Processing, 12, 826-37(2003).

[12] Han Yubing, Wu Lenan, Zhang Dongqing. Super-resolution reconstruction based on regularization processing[J]. Journal of Electronics & Information Technology, 29, 1713-1716(2007).

[13] Marquina A, Osher S J. Image super-resolution by TV-regularization and Bregman iteration[J]. Journal of Scientific Computing, 37, 367-382(2008).

[14] [14] Omer O A, Tanaka T. Regionbased weightednm approach to video superresolution with adaptive regularization[C] IEEE International Conference on Acoustics, 2009: 833836.

[15] Oliveira J P, BioucasDias J M, Figueiredo M A. Adaptive total variation image deblurring: A majorization-minimization approach.[J]. Signal Processing, 89, 1683-1693(2009).

[16] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 41, 613-627(1995).

[17] Candès Emmanuel J, Wakin M B, Boyd S P. Enhancing sparsity by reweighted l1 minimization[J]. Journal of Fourier Analysis & Applications, 14, 877-905(2008).

[18] [18] Jia C, Evans B L. Patchbased image deconvolution via joint modeling of sparse pris[C]IEEE International Conference on Image Processing, 2011: 681684.

[19] Dong W, Zhang L, Shi G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 20, 1838-1857(2011).

[20] David Leigh Donoho. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

[21] Mairal Julien. Incremental majorization-minimization optimization with application to large-scale machine learning[J]. SIAM Journal on Optimization, 25, 829-855(2015).

[22] [22] Papa G, Bianchi P, Clémençon S. Adaptive sampling f incremental optimization using stochastic gradient descent[C]International Conference on Algithmic Learning They, 2015.