Optics and Precision Engineering, Volume. 31, Issue 17, 2584(2023)
Multidimensional attention mechanism and selective feature fusion for image super-resolution reconstruction
[1] [1] 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
[2] ZOU W W, YUEN P C. Very low resolution face recognition problem[J]. IEEE Transactions on Image Processing, 21, 327-340(2012).
[3] SHI W, CABALLERO J, LEDIG C et al. Cardiac image super -resolution with global correspondence using multi -atlas patchmatch[C], 8151, 9-16(2013).
[4] [4] 朱福珍, 刘越, 黄鑫, 等. 改进的稀疏表示遥感图像超分辨重建[J]. 光学 精密工程, 2019, 27(3): 718-725. doi: 10.13482/j.issn1001-7011.2019.05.004ZHUF ZH, LIUY, HUANGX, et al. Remote sensing image super-resolution based on improved sparse representation[J]. Opt. Precision Eng., 2019, 27(3): 718-725.(in Chinese). doi: 10.13482/j.issn1001-7011.2019.05.004
[5] CHIANG M C, BOULT T E. Efficient image warping and super-resolution[C], 56-61(2002).
[6] HA V K, REN J C, XU X Y et al. Deep learning based single image super-resolution: a survey[J]. International Journal of Automation and Computing, 16, 413-426(2019).
[7] [7] 孙玉宝, 费选, 韦志辉, 等. 基于前向后向算子分裂的稀疏性正则化图像超分辨率算法[J]. 自动化学报, 2010, 36(9): 1232-1238. doi: 10.3724/sp.j.1004.2010.01232SUNY B, FEIX, WEIZH H, et al. Sparsity regularized image super-resolution model via forward-backward operator splitting method[J]. Acta Automatica Sinica, 2010, 36(9): 1232-1238.(in Chinese). doi: 10.3724/sp.j.1004.2010.01232
[8] [8] 潘宗序, 禹晶, 胡少兴, 等. 基于多尺度结构自相似性的单幅图像超分辨率算法[J]. 自动化学报, 2014, 40(4):594-603. doi: 10.3724/SP.J.1004.2014.02233PANZ X, YUJ, HUSH X, et al. Single image super resolution based on multi-scale structural self-similarity[J]. Acta Automatica Sinica, 2014, 40(4):594-603.(in Chinese). doi: 10.3724/SP.J.1004.2014.02233
[9] 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).
[10] DONG C, LOY C C, TANG X O. Accelerating the Super-resolution Convolutional Neural Network[M]. Computer Vision-ECCV 2016, 391-407(2016).
[11] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[12] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).
[13] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).
[14] TAI Y, YANG J, LIU X M et al. MemNet: a persistent memory network for image restoration[C], 4549-4557(2017).
[15] KIM H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).
[16] ZHANG Y L, LI K P, LI K et al. Image Super-resolution using Very Deep Residual Channel Attention Networks[M]. Computer Vision-ECCV 2018, 294-310(2018).
[17] [17] 程德强, 赵佳敏, 寇旗旗, 等. 多尺度密集特征融合的图像超分辨率重建[J]. 光学 精密工程, 2022, 30(20): 2489-2500. doi: 10.37188/OPE.20223020.2489CHENGD Q, ZHAOJ M, KOUQ Q, et al. Multi-scale dense feature fusion network for image super-resolution[J]. Opt. Precision Eng., 2022, 30(20): 2489-2500.(in Chinese). doi: 10.37188/OPE.20223020.2489
[18] LI J C, FANG F M, MEI K F et al. Multi-scale Residual Network for Image Super-resolution[M]. Computer Vision-ECCV 2018, 527-542(2018).
[19] KANG B, SOHN K A.
[20] LEDIG C, THEIS L, HUSZÁR F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).
[21] WANG X T, YU K, WU S X et al.
[22] TIAN C W, ZHUGE R B, WU Z H et al. Lightweight image super-resolution with enhanced CNN[J]. Knowledge-Based Systems, 205, 106235(2020).
[23] LI X, WANG W H, HU X L et al. Selective kernel networks[C], 510-519(2020).
[25] TIMOFTE R, AGUSTSSON E, VAN G L et al. NTIRE 2017 challenge on single image super-resolution: methods and results[C], 1110-1121(2017).
[26] BEVILACQUA M, ROUMY A, GUILLEMOT C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 10(2012).
[27] ZEYDE R, ELAD M, PROTTER M. On Single Image Scale-up using Sparse-Representations[M]. Curves and Surfaces, 711-730(2012).
[28] MARTIN D, FOWLKES C et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C], 416-423(2002).
[29] HUANG J B, SINGH A, AHUJA N. Single image super-resolution from transformed self-exemplars[C], 5197-5206(2015).
[30] ZHANG K, ZUO W M, ZHANG L. Learning a single convolutional super-resolution network for multiple degradations[C], 3262-3271(2018).
[31] WANG Z, BOVIK A C, SHEIKH H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).
[32] SUN J, XU Z B, SHUM H Y. Image super-resolution using gradient profile prior[C]. AK, 1-8(2008).
[33] TIMOFTE R, DE S V, VAN G L[M]. A+: Adjusted Anchored Neighborhood Regression for Fast Super-resolution, 111-126(2015).
[34] SCHULTER S, LEISTNER C, BISCHOF H. Fast and accurate image upscaling with super-resolution forests[C], 3791-3799(2015).
[36] ZHANG K, ZUO W M, CHEN Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[C], 3142-3155(2017).
[38] MAO X J, SHEN C H, YANG Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C], 2810-2818(2016).
[39] WANG Y F, WANG L J, WANG H Y et al. End-to-end image super-resolution via deep and shallow convolutional networks[C], 31959-31970(2019).
[40] YANG X, MEI H Y, ZHANG J Q et al. DRFN: deep recurrent fusion network for single-image super-resolution with large factors[C], 328-337(2018).
[41] LIU H, FU Z L, HAN J G et al. Single image super-resolution using multi-scale deep encoder-decoder with phase congruency edge map guidance[J]. Information Sciences, 473, 44-58(2019).
[42] TIAN C W, XU Y, ZUO W M et al. Coarse-to-fine CNN for image super-resolution[J]. IEEE Transactions on Multimedia, 23, 1489-1502(2021).
[43] TIAN C W, XU Y, ZUO W M et al. Asymmetric CNN for image superresolution[C], 3718-3730(2021).
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Jian WEN, Jianfei SHAO, Jie LIU, Jianlong SHAO, Yuhang FENG, Rong YE. Multidimensional attention mechanism and selective feature fusion for image super-resolution reconstruction[J]. Optics and Precision Engineering, 2023, 31(17): 2584
Category: Information Sciences
Received: Nov. 22, 2022
Accepted: --
Published Online: Oct. 9, 2023
The Author Email: Jianfei SHAO (469365367@qq.com)