[1] 1王德江, 孙翯, 孙雪倩. 消光比与探测器噪声对基于纳米线栅偏振成像系统偏振精度的影响[J]. 光学 精密工程, 2018, 26(10): 2371-2379. doi: 10.3788/ope.20182610.2371WANGD J, SUNH, SUNX Q. Effect of extinction ratio and detector noise on polarization accuracy of nanometer wire grid polarization imaging system[J]. Opt. Precision Eng., 2018, 26(10): 2371-2379.(in Chinese). doi: 10.3788/ope.20182610.2371
[2] 2王小龙, 王峰, 刘晓, 等. 荒漠背景下典型伪装目标的高光谱偏振特性[J]. 激光与光电子学进展, 2018, 55(5): 198-207. doi: 10.3788/lop55.051101WANGX L, WANGF, LIUX, et al. Hyperspectral polarization characteristics of typical camouflage target under desert background[J]. Laser & Optoelectronics Progress, 2018, 55(5): 198-207.(in Chinese). doi: 10.3788/lop55.051101
[3] 3王孙晨, 张磊, 薛模根, 等. 空间调制型全偏振成像系统解调算法优化[J]. 光子学报, 2020, 49(12): 152-162. doi: 10.3788/gzxb20204912.1211001WANGS CH, ZHANGL, XUEM G, et al. Optimization with demodulation algorithm for spatially modulated full polarization imaging system[J]. Acta Photonica Sinica, 2020, 49(12): 152-162.(in Chinese). doi: 10.3788/gzxb20204912.1211001
[4] 4胡巧云, 杨伟锋, 胡亚东, 等. 空间调制型全Stokes参量偏振成像系统原理及仿真[J]. 光学学报, 2015, 35(2): 152-158. doi: 10.3788/aos201535.0211004HUQ Y, YANGW F, HUY D, et al. Principle and simulation of a spatially modulated full stokes parameters polarization imaging system[J]. Acta Optica Sinica, 2015, 35(2): 152-158.(in Chinese). doi: 10.3788/aos201535.0211004
[5] 5王成, 范之国, 金海红, 等. 全偏振大气偏振模式成像系统的设计与优化分析[J]. 物理学报, 2021, 70(10): 123-131. doi: 10.7498/aps.70.20210104WANGC, FANZH G, JINH H, et al. Design and optimization analysis of imaging system of polarized skylight pattern of full polarization[J]. Acta Physica Sinica, 2021, 70(10): 123-131.(in Chinese). doi: 10.7498/aps.70.20210104
[6] 6吴艳, 陈凡胜, 陈桂林. 图像复原与超分辨率重构基本适用条件及提高空间分辨率上限的研究[J]. 红外与毫米波学报, 2010, 29(5): 351-356. doi: 10.3724/sp.j.1010.2010.00351WUY, CHENF SH, CHENG L. Applicable conditions and improving spatial resolution upper limits of image restoration and super-resolution reconstruction[J]. Journal of Infrared and Millimeter Waves, 2010, 29(5): 351-356.(in Chinese). doi: 10.3724/sp.j.1010.2010.00351
[7] 7吴笑天, 杨航, 孙兴龙. 基于区域选择网络的图像复原及其在计算成像中的应用[J]. 光学 精密工程, 2021, 29(4): 864-876. doi: 10.37188/OPE.20212904.0864WUX T, YANGH, SUNX L. Image restoring method based on region selection network and its application in computational imaging[J]. Opt. Precision Eng., 2021, 29(4): 864-876.(in Chinese). doi: 10.37188/OPE.20212904.0864
[8] 8徐国明, 袁宏武, 薛模根, 等. 空间调制全偏振计算成像场景迁移超分辨率方法[J]. 计算机辅助设计与图形学学报, 2021, 33(9): 1440-1449. doi: 10.3724/sp.j.1089.2021.18699XUG M, YUANH W, XUEM G, et al. Spatial modulation full polarization computing imaging super-resolution via scene transfer[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(9): 1440-1449.(in Chinese). doi: 10.3724/sp.j.1089.2021.18699
[9] J ZHANG, C G YUAN, G H HUANG et al. Acquisition of a full-resolution image and aliasing reduction for a spatially modulated imaging polarimeter with two snapshots. Applied Optics, 57, 2376-2382(2018).
[10] 10孟祥超, 孙伟伟, 任凯, 等. 基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合[J]. 遥感学报, 2020, 24(4): 379-387.MENGX CH, SUNW W, RENK, et al. Spatial-spectral fusion of GF-5/GF-1 remote sensing images based on multiresolution analysis[J]. Journal of Remote Sensing, 2020, 24(4): 379-387.(in Chinese)
[11] L ZHANG, J T NIE, W WEI et al. Unsupervised adaptation learning for hyperspectral imagery super-resolution, 3070-3079(2020).
[12] Q XIE, M H ZHOU, Q ZHAO et al. Multispectral and hyperspectral image fusion by MS/HS fusion net, 1585-1594(2019).
[13] J W SOH, S CHO, N I CHO. Meta-transfer learning for zero-shot super-resolution, 3513-3522(2020).
[14] Z H WANG, J CHEN, S C H HOI. Deep learning for image super-resolution: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3365-3387(2021).
[15] C DONG, C C LOY, K M HE et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).
[16] H KIM et al. Enhanced deep residual networks for single image super-resolution, 1132-1140(2017).
[17] J LIU, W J ZHANG, Y T TANG et al. Residual feature aggregation network for image super-resolution, 2356-2365(2020).
[18] Y L ZHANG, K P LI, K LI et al. Image super-resolution using very deep residual channel attention networks, 286-301(2018).
[19] R S LAN, L SUN, Z B LIU et al. MADNet: a fast and lightweight network for single-image super resolution. IEEE Transactions on Cybernetics, 51, 1443-1453(2021).
[20] Y H LI, Y IWAMOTO, L F LIN et al. VolumeNet: a lightweight parallel network for super-resolution of MR and CT volumetric data. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 30, 4840-4854(2021).
[21] J WAN, H YIN, Z H LIU et al. Lightweight image super-resolution by multi-scale aggregation. IEEE Transactions on Broadcasting, 67, 372-382(2021).
[22] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874-1883(2016).
[23] C DONG, C C LOY, X O TANG. Accelerating the super-resolution convolutional neural network, 391-407(2016).
[24] B KANG, K A SOHN. Fast, accurate, and lightweight super-resolution with cascading residual network, 252-268(2018).
[25] Z HUI, X M WANG, X B GAO. Fast and accurate single image super-resolution via information distillation network, 723-731(2018).
[26] J KIM, J K LEE, K M LEE. Deeply-recursive convolutional network for image super-resolution, 1637-1645(2016).
[27] Y TAI, J YANG, X M LIU. Image super-resolution via deep recursive residual network, 2790-2798(2017).
[28] C WANG, Z LI, J SHI. Lightweight image super-resolution with adaptive weighted learning network. arXiv preprint(2019).
[29] C W TIAN, Y XU, W M ZUO et al. Coarse-to-fine CNN for image super-resolution. IEEE Transactions on Multimedia, 23, 1489-1502(2021).
[30] W WEI, G Q FENG, Q ZHANG et al. Accurate single image super-resolution using cascading dense connections. Electronics Letters, 55, 739-742(2019).
[31] B KANG, K A SOHN. Photo-realistic image super-resolution with fast and lightweight cascading residual network. arXiv preprint arXiv, 2019.
[32] J HU, L SHEN, G SUN. Squeeze-and-excitation networks, 7132-7141(2018).
[33] T DAI, J R CAI, Y B ZHANG et al. Second-order attention network for single image super-resolution, 11057-11066(2019).
[34] Y T HU, J LI, Y F HUANG et al. Channel-wise and spatial feature modulation network for single image super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 30, 3911-3927(2020).
[35] B XU, N WANG, T CHEN et al. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv: 1505, 2015(00853).
[36] E AGUSTSSON, R TIMOFTE. NTIRE 2017 challenge on single image super-resolution: dataset and study, 1122-1131(2017).
[37] M HARIS, G SHAKHNAROVICH, N UKITA. Deep back-projection networks for super-resolution, 1664-1673(2018).
[38] 38蔡体健, 彭潇雨, 石亚鹏, 等. 通道注意力与残差级联的图像超分辨率重建[J]. 光学 精密工程, 2021, 29(1): 142-151. doi: 10.37188/OPE.20212901.0142CAIT J, PENGX Y, SHIY P, et al. Channel attention and residual concatenation network for image super-resolution[J]. Opt. Precision Eng., 2021, 29(1): 142-151.(in Chinese). doi: 10.37188/OPE.20212901.0142
[39] J LI, F FANG, K MEI et al. Multi-scale residual network for image super-resolution, 517-532(2018).