Improved blind motion deblurring method

Abstract

As one of several common blurs, motion blur images are quite common in life. There are many ways to remove motion blur, but each has some problems, such as checkerboard artifacts, poor restoration of image texture details, and high algorithm costs. This paper proposes an improved blind motion deblurring method with Generative Adversarial Network, which achieved good results. The method in this paper uses a combination of up-sampling and convolution to replace the deconvolution of the traditional generative adversarial network model, effectively removing the common checkerboard artifacts in image processing, and the problems of poor texture detail restoration. The results show that the method in the article has achieved the expected purpose, it has achieved obvious deblurring effects from both objective and subjective evaluation indicators.