Abstract
Abstract 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.
Highlights
Humans rely on the visual system to obtain a large amount of information
The second is to simulate the gradient distribution of the image through mathematical modeling, and further study the image deblurring The research object of this paper is motion blur, which is the image blur caused by lens out of focus, object movement, camera shake and other factors dublurring the shooting process[3].For motion blur, equipment can be avoided by using a sports camera
Non-blind deblurring is performed under the premise that the blur kernel is known, and a clear image is obtained by deconvolution of the blurred image and the blur kernel
Summary
Humans rely on the visual system to obtain a large amount of information. Studies have shown that about 70% of the information is obtained through the visual system. The second is to simulate the gradient distribution of the image through mathematical modeling, and further study the image deblurring The research object of this paper is motion blur, which is the image blur caused by lens out of focus, object movement, camera shake and other factors dublurring the shooting process[3].For motion blur, equipment can be avoided by using a sports camera Such equipment is generally expensive, ten times or even dozens of times the price of ordinary cameras, and it is difficult to popularize and use it on a large scale. Image deblurring is a serious ill posed problem, because in the process of solving, due to the interference of unknown fuzzy kernel and noise and other factors, the difficulty of image motion blur algorithm is increasing. Based on the inspiration of DeblurGAN [6], the method of combining the counter loss function and the multi-scale function is adopted to adjust the network parameters and train a stable network to achieve the research purpose
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