一种基于深度残差网络的图像修复算法设计与实现

doi:10.19640/j.cnki.jtau.2024.03.015

Journal of Tianjin Agricultural University ›› 2024, Vol. 31 ›› Issue (3): 85-91.doi: 10.19640/j.cnki.jtau.2024.03.015

• Researches and Scientific Notes • Previous Articles Next Articles

Design and implementation of an image inpainting algorithm based on deep residual network

Wu Jinmiao¹, Wang Yuxin^{1,Corresponding Author}, Han Jiangning², Zhang Jialiang¹, Zhang Zhi¹, Wei Yulu¹

1. College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China;
2. Business Management Department, China Unicom Video Technology Co., Ltd., Tianjin 300300, China

Received:2022-10-25 Online:2024-06-30 Published:2024-07-15

Abstract

Abstract: With the increasing use of digital images in people's daily life, restoring the missing areas of damaged images has become a problem, for which this paper proposes an image inpainting model based on a deep residual network. The overall architecture of the model is based on the encoder-decoder structure. The encoder uses residual networks of different depths, and the decoder uses a deconvolution network structure and an upsampling-convolution structure. The effects of different structure decoders, encoders and different loss functions on the image inpainting effect in this model were discussed through experiments. The experimental results showed that the image inpainting model based on the deep residual network proposed in this paper could achieve good image inpainting effect by adopting the modified Resnet 34-layer as the encoder, the deconvolution network as the decoder, and the L1 Loss as the loss function.

Key words: image inpainting, residual network, deconvolution, encoder-decoder

CLC Number:

TP391.4

Wu Jinmiao, Wang Yuxin, Han Jiangning, Zhang Jialiang, Zhang Zhi, Wei Yulu. Design and implementation of an image inpainting algorithm based on deep residual network[J]. Journal of Tianjin Agricultural University, 2024, 31(3): 85-91.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://xuebao.tjau.edu.cn/EN/10.19640/j.cnki.jtau.2024.03.015

http://xuebao.tjau.edu.cn/EN/Y2024/V31/I3/85

References

[1] 李权锋. 图像修复研究进展综述[J]. 计算机时代,2021(12):6-9.
[2] 赵露露,沈玲,洪日昌. 图像修复研究进展综述[J]. 计算机科学,2021,48(3):14-26.
[3] PATHAK D,KRAHENBUHL P,DONAHUE J,et al.Context encoders:Feature learning by inpainting[C]// IEEE. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington DC:IEEE Computer Society,2016:2536-2544.
[4] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//IEEE. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington DC:IEEE Computer Society,2016:770-778.
[5] ODENA A,DUMOULIN V,OLAH C.Deconvolution and checkerboard artifacts[J]. Distill,2016,1(10):143.
[6] 张焕,张庆,于纪言. 激活函数的发展综述及其性质分析[J]. 西华大学学报(自然科学版),2021,40(4):1-10.
[7] PHILBIN J,CHUM O,ISARD M,et al.Lost in quantization:Improving particular object retrieval in large scale image databases[C]//IEEE. 2008 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2008:1-8.
[8] DOZAT T.Incorporating nesterov momentum into Adam[C]//ICLR. Proceedings of ICLR 2016 Workshop. Addis Ababa:ICLR,2016:1-4.
[9] YU J H,LIN Z,YANG J M,et al.Generative image inpainting with contextual attention[C]//IEEE. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE,2018:5505-5514.
[10] CHUNG G,VESE L A.Image segmentation using a multilayer level-set approach[J]. Computing and Visualization in Science,2009,12(6):267-285.
[11] HORE A,ZIOU D.Image quality metrics:PSNR vs. SSIM[C]//IEEE. 20th International Conference on Pattern Recognition. Piscataway:IEEE,2010:2366-2369.
[12] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J]. Computer Science,2014,23:1409.

Design and implementation of an image inpainting algorithm based on deep residual network

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 8

Recommended Articles

Metrics

Comments

[1]	An Ruiyu, Hao Zhibin. Regional segmentation method for wheat leaf diseases and insect pests based on semi-supervised active learning [J]. Journal of Tianjin Agricultural University, 2024, 31(2): 87-93.
[2]	Ma Hanzhang, Wang Miaoxin, Zhong Pinyang, Liu Hua, Chang Ruokui, Dong Jinfeng. Design of garbage classification system based on visual recognition [J]. Journal of Tianjin Agricultural University, 2023, 30(4): 53-57.
[3]	Zheng Haibo, Song Shuang, Liu Tonghai. Traffic sign detection method based on improved Tiny YOLOv3 [J]. Journal of Tianjin Agricultural University, 2023, 30(2): 62-68.
[4]	Gao Jiaqi, Wu Fang. Research on classification of rural water quality and color based on machine learning [J]. Journal of Tianjin Agricultural University, 2022, 29(1): 61-65.
[5]	JI Si-qi, WU Fang, LI Nai-xiang. Static Early-warning Model of Vegetable DiseasesBased on Decision Tree [J]. Journal of Tianjin Agricultural University, 2017, 24(2): 77-80.
[6]	WANG Qi-xin, DENG Sheng-lin. A New Edge Detection Method for Chromosome Image [J]. Journal of Tianjin Agricultural University, 2012, 19(1): 42-44.
[7]	ZHAO Na, ZHAO Ping, GAO Yi-jun. Study on Application of Machine Vision Technology to Modern Agriculture in China [J]. Journal of Tianjin Agricultural University, 2015, 22(2): 55-58.
[8]	MA Guo-qiang, MA Ji-fei, GUO Peng, SONG Zhi-heng, YU Na. Implementation of Improved K-means Clustering Segmentation Algorithm for Medical CT Image [J]. Journal of Tianjin Agricultural University, 2013, 20(4): 28-35.