基于广度残差与像素点注意力的图像去模糊模型

doi:10.3969/j.issn.0255-8297.2022.06.010

Abstract

Abstract: In order to solve the problem that existing methods suffer difficulty in quickly recovering high-quality sharp images from blurred images, an image deblurring model based on width residual and pixel attention is proposed. Based on encoder-decoder networks, the model uses wide convolution and multi-order residual method to construct width residual modules, improving the processing speed of the model. At the same time, local average and matrix cross multiplication are used to construct pixel attention modules, which enhance the model deblurring quality. The experimental results on GOPRO datasets show that the structural similarity of the proposed method is 0.922 3, the peak signal-to-noise ratio is 31.74 dB, and the average running time is 0.37 seconds when the model size is 22.24 MB. Compared with the scale-recurrent network method, the peak signal-to-noise ratio of the proposed method improves by 4%, and its performance is better than the other existing deblurring methods.

Key words: image deblurring, encoder-decoder network, pixel attention, width residual

CLC Number:

TP391.4

KUANG Fa, XIONG Bangshu, OU Qiaofeng, YU Lei. Image Deblurring Model Based on Width Residual and Pixel Attention[J]. Journal of Applied Sciences, 2022, 40(6): 996-1005.

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

URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2022.06.010

https://www.jas.shu.edu.cn/EN/Y2022/V40/I6/996

[1] Chen L, Fang F, Wang T, et al. Blind image deblurring with local maximum gradient prior[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:1742-1750.
[2] Yang L, Ji H. A variational EM framework with adaptive edge selection for blind motion deblurring[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:10167-10176.
[3] 刘鹏飞, 肖亮. 基于Hessian核范数正则化的快速图像复原算法[J]. 电子学报, 2014, 43(10):2001- 2008. Liu P F, Xiao L. A fast algorithm for image restoration based on Hessian nuclear norm regularization[J]. Acta Electronica Sinica, 2014, 43(10):2001-2008. (in Chinese)
[4] Xu L, Zheng S, Jia J. Unnatural l0 sparse representation for natural image deblurring[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013:1107-1114.
[5] 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, 2017:3883-3891.
[6] Tao X, Gao H, Shen X, et al. Scale-recurrent network for deep image deblurring[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:8174-8182.
[7] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8):1735-1780.
[8] Zhang H, Dai Y, Li H, et al. Deep stacked hierarchical multi-patch network for image deblurring[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:5978-5986.
[9] Suin M, Purohit K, Rajagopalan A N. Spatially-attentive patch-hierarchical network for adaptive motion deblurring[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:3606-3615.
[10] Kupyn O, Budzan V, Mykhailych M, et al. Deblurgan:blind motion deblurring using conditional adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:8183-8192.
[11] Kupyn O, Martyniuk T, Wu J, et al. Deblurgan-v2:deblurring (orders-of-magnitude) faster and better[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019:8878-8887.
[12] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778.
[13] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:4700-4708.
[14] Yu J, Fan Y, Huang T. Wide activation for efficient image and video super-resolution[C]//30th British Machine Vision Conference, 2019:1-13.
[15] Gao H, Tao X, Shen X, et al. Dynamic scene deblurring with parameter selective sharing and nested skip connections[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:3848-3856.
[16] Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:7794-7803.
[17] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:7132-7141.
[18] Roy A G, Navab N, Wachinger C. Concurrent spatial and channel squeeze & excitation in fully convolutional networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018:421-429.
[19] Woo S, Park J, Lee J Y, et al. CBAM:convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018:3-19.
[20] Cao Y, Xu J, Lin S, et al. Gcnet:non-local networks meet squeeze-excitation networks and beyond[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019:1971-1980.
[21] Zhang J, Pan J, Ren J, et al. Dynamic scene deblurring using spatially variant recurrent neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:2521-2529.
[22] Li L, Pan J, Lai W S, et al. Dynamic scene deblurring by depth guided model[J]. IEEE Transactions on Image Processing, 2020, 29:5273-5288.
[23] Feng H, Guo J, Xu H, et al. SharpGAN:dynamic scene deblurring method for smart ship based on receptive field block and generative adversarial networks[J]. Sensors, 2021, 21(11):3641-3659.
[24] Shen Z, Wang W, Lu X, et al. Human-aware motion deblurring[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019:5572-5581.

Image Deblurring Model Based on Width Residual and Pixel Attention

PDF

Knowledge

Abstract

Cite this article

share this article

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	JIANG Zetao, JIN Xin, LENG Lu, ZHU Wencai. A Low-Light Few-Shot Object Detection Method Based on Feature Optimization [J]. Journal of Applied Sciences, 2026, 44(3): 422-436.
[2]	GAO Guangliang, LIANG Weichao, ZHU Tao, HONG Lei, XIA Lingling. Multi-modal Rumor Detection Method Fusing Image and Text Features [J]. Journal of Applied Sciences, 2026, 44(3): 503-514.
[3]	LI Yongzhen, MA Fuyuan, MA Shixuan, WANG Yuhan, WANG Ying. Network Community Detection Based on Structure-Enhanced Deep Clustering [J]. Journal of Applied Sciences, 2026, 44(1): 1-20.
[4]	JIN Zhengyang, YAN Shaohong, ZHANG Yanbo, YAO Xulong, TAO Zhigang, CHEN Zhiyuan. Three-Dimensional Fuzzy Clustering Algorithm Integrating Spatial Texture Features [J]. Journal of Applied Sciences, 2026, 44(1): 134-148.
[5]	WANG Jingwei, WANG Haihua, WU Hao, LUO Xiangyang, MA Bin. Improvement of Adversarial Transferability via Transferability Gap [J]. Journal of Applied Sciences, 2025, 43(5): 799-807.
[6]	ZHANG Ailing, YANG Linying, YAN Shiju. Joint Grading of Diabetic Retinopathy and Diabetic Macular Edema Based on ResNeSt Network [J]. Journal of Applied Sciences, 2025, 43(5): 849-862.
[7]	WANG Jinwei, ZHANG Meigui, ZHANG Jiawei, LUO Xiangyang, MA Bin. Frequency-Domain Multi-feature Fusion for Deepfake Video Detection Based on Key Frames [J]. Journal of Applied Sciences, 2025, 43(3): 451-462.
[8]	YANG Wan, CHEN Aibin, ZHAO Ying, WU Yue, ZHEN Xin, XIAO Zhishu. Leopards Individual Recognition Based on Bi-level Routing Attention and Self-Calibrated Convolution [J]. Journal of Applied Sciences, 2025, 43(2): 348-360.
[9]	DENG Shuchong, CHEN Aibin, DAI Zijian. Bird Action Recognition Based on Multiple Excitation and Pyramid Split Attention [J]. Journal of Applied Sciences, 2025, 43(1): 154-168.
[10]	HE Jiabei, ZHOU Juxiang, GAN Jianhou, WU Di, WEN Xiaoyu. Classroom Expression Classification Model Based on Multitask Learning [J]. Journal of Applied Sciences, 2024, 42(6): 947-961.
[11]	LI Sha, WANG Yongxiong, WANG Zhe, CHEN Xu, HE Jiaxin. Casting Defect Detection Based on Local and Global Features [J]. Journal of Applied Sciences, 2024, 42(5): 757-768.
[12]	HUA Yitan, HUANG Yingping, GUO Wenhao. Fusion of Point-Cloud and Image for Road Segmentation Using CNN and Transformer [J]. Journal of Applied Sciences, 2024, 42(4): 695-708.
[13]	JIN Yanliang, WU Xiaowei. Sign Language Recognition Based on Two-Stream Adaptive Enhanced Spatial Temporal Graph Convolutional Network [J]. Journal of Applied Sciences, 2024, 42(2): 189-199.
[14]	CUI Shuaihua, YU Lei, HE Xi, XIONG Bangshu, OU Qiaofeng. A Large FOV Convergence Binocular Stereo Vision Calibration Method [J]. Journal of Applied Sciences, 2024, 42(2): 269-279.
[15]	LI Shaoqian, CHENG Xin, ZHOU Jingmei, ZHAO Xiangmo. Object Tracking Algorithm Based on Vehicle Appearance Features and Inter-frame Optical Flow [J]. Journal of Applied Sciences, 2024, 42(1): 103-118.