基于生成对抗网络的图像超分辨率重建算法

刘郭琦; 刘进锋; 朱东辉

doi:10.37188/CJLCD.2021-0227

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基于生成对抗网络的图像超分辨率重建算法

图像处理 | 更新时间：2021-12-10

- 基于生成对抗网络的图像超分辨率重建算法
- Image super resolution reconstruction algorithm based on generative countermeasure network
- 液晶与显示 2021年36卷第12期页码：1720-1727
- 作者机构：
  
  宁夏大学信息工程学院, 宁夏银川 750021
- 作者简介：
  
  [ "刘郭琦(1992-), 男, 宁夏银川人, 硕士, 2021年于宁夏大学获得硕士学位, 研究方向为数字图像处理、深度学习。E-mail: 474313871@qq.com" ]
  [ "刘进锋(1971-), 男, 宁夏中宁人, 博士, 教授, 2013年于西北工业大学获得博士学位, 研究方向为数字图像处理、深度学习、异构计算等。E-mail: jfliu@nxu.edu.cn" ]
- 基金信息：
  
  宁夏自然科学基金(2021AAC03084)
- DOI：10.37188/CJLCD.2021-0227
  中图分类号： TP391.4
- 收稿日期：2021-09-01，
  
  修回日期：2021-09-19，
  
  纸质出版日期：2021-12
- 稿件说明：
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刘郭琦, 刘进锋, 朱东辉. 基于生成对抗网络的图像超分辨率重建算法[J]. 液晶与显示, 2021,36(12):1720-1727.

Guo-qi LIU, Jin-feng LIU, Dong-hui ZHU. Image super resolution reconstruction algorithm based on generative countermeasure network[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1720-1727.
刘郭琦, 刘进锋, 朱东辉. 基于生成对抗网络的图像超分辨率重建算法[J]. 液晶与显示, 2021,36(12):1720-1727. DOI： 10.37188/CJLCD.2021-0227.

Guo-qi LIU, Jin-feng LIU, Dong-hui ZHU. Image super resolution reconstruction algorithm based on generative countermeasure network[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1720-1727. DOI： 10.37188/CJLCD.2021-0227.

摘要

SRGAN是基于深度学习的图像超分辨率的典型方法，重建效果较好，但该算法还存在一些缺陷，在提高图像质量和运行速度上仍然有较大提升空间。本文在SRGAN网络模型的基础上提出了一个优化模型。因为批量归一化（BN）层在超分辨图像重建中常常会忽略一些图像的细节，同时增加网络的复杂度，所以在SRGAN的生成器中去除了BN层，并引入ECA通道注意力，使每个残差块生成特征图获得相应的权重，以便处理更多的图像细节。经过公开数据集的训练和对比实验，结果表明提出的改进模型相比于对比模型，重建图像的细节恢复更丰富，视觉效果更好，峰值信噪比和结构相似性表现更佳，模型总参数量更少。

Abstract

SRGAN is a typical method of image super-resolution based on deep learning

the reconstruction effect is good

but the algorithm still has some shortcomings

and there is still more room for improving the image quality and operation speed. An optimization model is proposed based on the SRGAN network model. Because the batch normalization (BN) layer often ignores some image details in super-resolution image reconstruction and increases the complexity of the network at the same time

the BN layer is removed from the generator of SRGAN and the ECA channel attention is introduced so that each residual block generating feature map gets a corresponding weight in order to process more image details. After training and comparison experiments on public datasets

the results show that the proposed improved model has richer image details recovery

better visual effects

better peak signal-to-noise ratio and structural similarity performance

and fewer total number of model parameters compared to the comparison model.

关键词

Keywords

references

邵洪禹 , 李英超 , 王超 , 等 . 超分辨望远光学系统像差影响及优化设计 . 中国光学 , 2020 . 13 ( 1 ): 106 - 120 . DOI: 10.3788/CO.20201301.0106 http://doi.org/10.3788/CO.20201301.0106 .

H Y SHAO , Y C LI , C WANG , 等 . Aberration effect and optimization design of super-resolution telescope optical system . Chinese Optics , 2020 . 13 ( 1 ): 106 - 120 . DOI: 10.3788/CO.20201301.0106 http://doi.org/10.3788/CO.20201301.0106 .

A KAPPELER , S YOO , Q Q DAI , 等 . Video super-resolution with convolutional neural networks . IEEE Transactions on Computational Imaging , 2016 . 2 ( 2 ): 109 - 122 . DOI: 10.1109/TCI.2016.2532323 http://doi.org/10.1109/TCI.2016.2532323 .

JADERBERG M, SIMONYAN K, ZISSERMAN A. Spatial transformer networks[C]// Proceedings of the 28 th International Conference on Neural Information Processing Systems . Montreal: ACM, 2015: 2017-2025.

IRANI M, PELEG S. Super resolution from image sequences[C]//10th International Conference on Pattern Recognition. Atlantic City: IEEE, 1990: 115-120.

H STARK , P OSKOUI . High-resolution image recovery from image-plane arrays, using convex projections . Journal of the Optical Society of America A , 1989 . 6 ( 11 ): 1715 - 1726 . DOI: 10.1364/JOSAA.6.001715 http://doi.org/10.1364/JOSAA.6.001715 .

赵秀影 , 苏耘 , 董艳芹 , 等 . 一种基于小波与双三次插值的CCD图像超分辨方法 . 计算机应用研究 , 2009 . 26 ( 6 ): 2365 - 2367 . DOI: 10.3969/j.issn.1001-3695.2009.06.110 http://doi.org/10.3969/j.issn.1001-3695.2009.06.110 .

X Y ZHAO , Y SU , Y Q DONG , 等 . Kind of super-resolution method of CCD image based on wavelet and bicubic interpolation . Application Research of Computers , 2009 . 26 ( 6 ): 2365 - 2367 . DOI: 10.3969/j.issn.1001-3695.2009.06.110 http://doi.org/10.3969/j.issn.1001-3695.2009.06.110 .

张永红 , 段东海 . 基于邻近均值图像插值的数字图像隐藏技术 . 微计算机应用 , 2009 . 30 ( 6 ): 39 - 42 . DOI: 10.3969/j.issn.2095-347X.2009.06.008 http://doi.org/10.3969/j.issn.2095-347X.2009.06.008 .

Y H ZHANG , D H DUAN . Digital image hiding technology based on neighbor mean image interpolation . Microcomputer Applications , 2009 . 30 ( 6 ): 39 - 42 . DOI: 10.3969/j.issn.2095-347X.2009.06.008 http://doi.org/10.3969/j.issn.2095-347X.2009.06.008 .

DONG C, LOY C C, HE K M, et al . Learning a deep convolutional network for image super-resolution[C]//13 th European Conference on Computer Vision . Zurich: Springer, 2014: 184-199.

KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 1646-1654.

IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]// Proceedings of the 32 nd International Conference on International Conference on Machine Learning . Lille: ACM, 2015: 448-456.

LIM B, SON S, KIM H, et al . Enhanced deep residual networks for single image super-resolution[C] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Honolulu: IEEE, 2017: 1132-1140.

LAI W S, HUANG J B, AHUJA N, et al . Deep Laplacian pyramid networks for fast and accurate super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 5835-5843.

ZHANG Y L, TIAN Y P, KONG Y, et al . Residual dense network for image super-resolution[C]// Proceedings of the 2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City: IEEE, 2018: 2472-2481.

LEDIG C, THEIS L, HUSZÁR F, et al . Photo-realistic single image super-resolution using a generative adversarial network[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 105-114.

X W XU , J W WANG , B F ZHONG , 等 . Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism . Measurement , 2021 . 177 109254 DOI: 10.1016/j.measurement.2021.109254 http://doi.org/10.1016/j.measurement.2021.109254 .

WANG Q L, WU B G, ZHU P F, et al . ECA-Net: efficient channel attention for deep convolutional neural networks[C]//2020 IEEE / CVF Conference on Computer Vision and Pattern Recognition ( CVPR ). Seattle: IEEE, 2020: 11531-11539.

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