Motion Blur Image Restoration by Multi-Scale Residual Neural Network

• Published in: International journal of advanced network, monitoring, and controls Vol. 6; no. 1; pp. 57 - 67
• Main Authors: Hexin, Xu, Li, Zhao, Yan, Jiao
• Format: Journal Article
• Language: English
• Published: Xi'an Sciendo 01.01.2021; De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
• Subjects: Algorithms; Cameras; Computer vision; Deep Learning; Image Deblur; Motion Deblur; Multi-Scale Residual Network; Neural networks
• ISSN: 2470-8038; 2470-8038
• DOI: 10.21307/ijanmc-2021-009

Blind deblurring is a basic subject of computer vision and image processing. Motion image deblurring is divided into non blind deblurring and blind deblurring by whether to estimate the blur kernel. Blind deblurring is easy to produce motion artifacts because of the inaccurate estimation of the blur kernel. Non blind deblurring is the best choice for the current blurred image processing. The purpose of this paper is to further improve the definition of blurred image, restore the edge information of contour, and strengthen the repair of texture details. Based on the multi-scale convolution neural network, a multi-scale residual network is proposed, which can comprehensively extract image features, enhance image feature fusion, and constrain image generation by combining multi-scale loss function with anti loss function. The performance of the algorithm is evaluated by testing the peak signal to noise ratio (PSNR) structure similarity and restoration time of the generated image relative to the clear image. This algorithm improves the average PSNR on GOPRO testset, and reduces the recovery time accordingly. It can successfully recover the detail information lost due to motion blur. This algorithm has simple network structure, strong robustness and good restoration effect, and is suitable for dealing with various image degradation problems caused by motion blur.