[1]林椹尠,张梦凯,吴成茂.利用残差密集网络的运动模糊复原方法[J].智能系统学报,2021,16(3):442-448.[doi:10.11992/tis.201912002]
 LIN Zhenxian,ZHANG Mengkai,WU Chengmao.Image restoration with residual dense network[J].CAAI Transactions on Intelligent Systems,2021,16(3):442-448.[doi:10.11992/tis.201912002]
点击复制

利用残差密集网络的运动模糊复原方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年3期
页码:
442-448
栏目:
学术论文—机器感知与模式识别
出版日期:
2021-05-05

文章信息/Info

Title:
Image restoration with residual dense network
作者:
林椹尠1 张梦凯2 吴成茂3
1. 西安邮电大学 理学院,陕西 西安 710121;
2. 西安邮电大学 通信与信息工程学院,陕西 西安 710121;
3. 西安邮电大学 电子工程学院,陕西 西安 710121
Author(s):
LIN Zhenxian1 ZHANG Mengkai2 WU Chengmao3
1. School of Science, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
2. School of Communication and Information Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
3. School of Electronic Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China
关键词:
图像处理运动图片图像去噪图像复原深度学习网络模型网络结构卷积神经网络
Keywords:
image processingmotion picturesimage denoisingimage restorationdeep learningnetwork modelnetwork architectureconvolutional neural network
分类号:
TP183
DOI:
10.11992/tis.201912002
摘要:
针对图像生成过程中由于物体运动或相机抖动产生的运动模糊问题,提出了利用残差密集网络的运动模糊图像复原方法。设计对抗网络结构,以残差密集网络为生成器,通过长短连接实现不同层次特征的融合,生成复原图像,以深度卷积网络为判别器,判断图像真伪,在生成器和判别器的对抗中提高网络性能;采用对抗损失和内容损失结合的损失函数,提高网络的复原效果;以端到端的方式,省略模糊核的估计过程,输入模糊图像直接获取复原图像。实验结果表明,该方法能够取得较好的复原效果。
Abstract:
Aiming at the problem of motion blur caused by object motion or camera shake in the image generation process, an image restoration method based on the residual dense network is proposed. An adversarial network structure is used in the method, taking the residual dense network as the generator and fusing different levels of features in a long-short connection to generate restored images. The deep convolution network acts as the discriminator to identify the authenticities of images, training the network performance in adversarial between the generator and discriminator. The proposed method combines the adversarial and content losses to improve the effectiveness of network restoration. This study demonstrates the restoration of an input blur image in an end-to-end way without estimating the blur kernel. Experiments show that the proposed method results in a better restoration effect.

参考文献/References:

[1] 程俊廷, 左旺孟. 快速非均匀模糊图像的盲复原模型[J]. 黑龙江科技大学学报, 2017, 27(2):196-199
CHENG Junting, ZUO Wangmeng. Fast blind deblurring models for restoration of non-uniform blur images[J]. Journal of Heilongjiang University of Science and Technology, 2017, 27(2):196-199
[2] BANHAM M R, KATSAGGELOS A K. Digital image restoration[J]. IEEE signal processing magazine, 1997, 14(2):24-41.
[3] ZHOU Y T, CHELLAPPA R, VAID A, et al. Image restoration using a neural network[J]. IEEE transactions on acoustics, speech, and signal processing, 1988, 36(7):1141-1151.
[4] JAIN V, SEUNG S. Natural image denoising with convolutional networks[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2009:769-776.
[5] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:2261-2269.
[6] ZHANG Kai, ZUO Wangmeng, CHEN Yunjin, et al. Beyond a Gaussian denoiser:residual learning of deep CNN for image denoising[J]. IEEE transactions on image processing, 2017, 26(7):3142-3155.
[7] LIU Ding, WEN Bihan, FAN Yuchen, et al. Non-local recurrent network for image restoration[C]//32nd Conference on Neural Information Processing Systems. Montréal, Canada, 2018:1673-1682.
[8] GUO Shi, YAN Zifei, ZHANG Kai, et al. Toward convolutional blind denoising of real photographs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019:1712-1722.
[9] CHA S, MOON T. Fully convolutional pixel adaptive image denoiser[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South), 2019:4159-4168.
[10] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada, 2014:2672-2680.
[11] KUPYN O, BUDZAN V, MYKHAILYCH M, et al. DeblurGAN:blind motion deblurring using conditional adversarial networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:8183-8192.
[12] WANG M, LI H, LI F. Generative adversarial network based on resnet for conditional image restoration[EB/OL].[2021?06?07] https://arxiv.org/abs/1707.04881.
[13] ZHOU H, SUN J, YACOOB Y, et al. Label denoising adversarial network (LDAN) for inverse lighting of faces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:6238-6247.
[14] ZHANG Yulun, TIAN Yapeng, KONG Yu, et al. Residual dense network for image super-resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:2472-2481.
[15] SAJJADI M S M, SCH?LKOPF B, HIRSCH M. EnhanceNet:single image super-resolution through automated texture synthesis[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017:4501-4510.
[16] ADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL].[2021?06?07] https://arxiv.org/abs/1511.06434.
[17] ZHAO Hang, GALLO O, FROSIO I, et al. Loss functions for image restoration with neural networks[J]. IEEE transactions on computational imaging, 2017, 3(1):47-57.
[18] WHYTE O, SIVIC J, ZISSERMAN A, et al. Non-uniform deblurring for shaken images[J]. International journal of computer vision, 2012, 98(2):168-186.
[19] NAH S, KIM T H, LEE K M. Deep multi-scale convolutional neural network for dynamic scene deblurring[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:257-265.
[20] BENGIO Y, LOURADOUR J, COLLOBERT R, et al. Curriculum learning[C]//Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Canada, 2009:41-48.
[21] 佟雨兵, 张其善, 祁云平. 基于PSNR与SSIM联合的图像质量评价模型[J]. 中国图象图形学报, 2006, 11(12):1758-1763
TONG Yubing, ZHANG Qishan, QI Yunping. Image quality assessing by combining PSNR with SSIM[J]. Journal of image and graphics, 2006, 11(12):1758-1763
[22] WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE transactions on image processing, 2004, 13(4):600-612.
[23] TAO Xin, GAO Hongyun, SHEN Xiaoyong, et al. Scale-recurrent network for deep image deblurring[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:8174-8182.

相似文献/References:

[1]李庆武,蔡艳梅,徐立中.基于分块分类的智能视频监控背景更新算法[J].智能系统学报,2010,5(03):272.
 LI Qing-wu,CAI Yan-mei,XU Li-zhong.Background update algorithm based on blocks classification forintelligent video surveillance[J].CAAI Transactions on Intelligent Systems,2010,5(3):272.
[2]邵真天,袁杰.一种基于曲波变换的图像去块算法[J].智能系统学报,2012,7(02):102.
 SHAO Zhentian,YUAN Jie.An image deblocking algorithm based on curvelet transformation[J].CAAI Transactions on Intelligent Systems,2012,7(3):102.
[3]王锦榕,袁学海,刘增良.基于图像处理技术的瞳孔和角膜反射中心提取算法[J].智能系统学报,2012,7(05):423.
 WANG Jinrong,YUAN Xuehai,LIU Zengliang.An extraction method of pupil and corneal reflection centers based on image processing technology[J].CAAI Transactions on Intelligent Systems,2012,7(3):423.
[4]傅博,姜勇,王洪光,等.输电线路巡检图像智能诊断系统[J].智能系统学报,2016,11(1):70.[doi:10.11992/tis.201503043]
 FU Bo,JIANG Yong,WANG Hongguang,et al.Intelligent diagnosis system for patrol check images of power transmission lines[J].CAAI Transactions on Intelligent Systems,2016,11(3):70.[doi:10.11992/tis.201503043]
[5]任晓霞,孙秀明,耿鹏,等.多小波和NSDFB组合域递归滤波多聚焦图像融合[J].智能系统学报,2016,11(2):241.[doi:10.11992/tis.201509017]
 REN Xiaoxia,SUN Xiuming,GENG Peng,et al.Multifocus image fusion using a recursive filter in the combined domain of multiwavelets and NSDFB[J].CAAI Transactions on Intelligent Systems,2016,11(3):241.[doi:10.11992/tis.201509017]
[6]梁义辉,战强.一种面向无线图像传输的视觉平台[J].智能系统学报,2016,11(5):608.[doi:10.11992/tis.201512014]
 LIANG Yihui,ZHAN Qiang.A visual platform for wireless image transmission[J].CAAI Transactions on Intelligent Systems,2016,11(3):608.[doi:10.11992/tis.201512014]
[7]李霞丽,吴立成,樊艳明.易于硬件实现的压缩感知观测矩阵的研究与构造[J].智能系统学报,2017,12(03):279.[doi:10.11992/tis.201606037]
 LI Xiali,WU Licheng,FAN Yanming.Study and construction of a compressed sensing measurement matrix that is easy to implement in hardware[J].CAAI Transactions on Intelligent Systems,2017,12(3):279.[doi:10.11992/tis.201606037]
[8]姜婷,袭肖明,岳厚光.基于分布先验的半监督FCM的肺结节分类[J].智能系统学报,2017,12(05):729.[doi:10.11992/tis.201706018]
 JIANG Ting,XI Xiaoming,YUE Houguang.Classification of pulmonary nodules by semi-supervised FCM based on prior distribution[J].CAAI Transactions on Intelligent Systems,2017,12(3):729.[doi:10.11992/tis.201706018]
[9]王科俊,赵彦东,邢向磊.深度学习在无人驾驶汽车领域应用的研究进展[J].智能系统学报,2018,13(01):55.[doi:10.11992/tis.201609029]
 WANG Kejun,ZHAO Yandong,XING Xianglei.Deep learning in driverless vehicles[J].CAAI Transactions on Intelligent Systems,2018,13(3):55.[doi:10.11992/tis.201609029]
[10]刘彪,黄蓉蓉,林和,等.基于卷积神经网络的盲文音乐识别研究[J].智能系统学报,2019,14(1):186.[doi:10.11992/tis.201805002]
 LIU Biao,HUANG Rongrong,LIN He,et al.Research on braille music recognition based on convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2019,14(3):186.[doi:10.11992/tis.201805002]

备注/Memo

备注/Memo:
收稿日期:2019-12-02。
基金项目:国家自然科学基金项目(61671377)
作者简介:林椹尠,教授,博士,主要研究方向为基于小波理论的图像处理。发表学术论文30余篇;张梦凯,硕士研究生,主要研究方向为图像处理;吴成茂,高级工程师,主要研究方向为模式分析与智能信息处理、图像处理与信息安全。发表学术论文200余篇
通讯作者:张梦凯.E-maili:zmkdyx@163.com
更新日期/Last Update: 2021-06-25