Investigation on the blurred image restoration based on brain‐inspired model - Meng Xiangyan; Zhao Chen; Zhao Li; Yan Keding; Ren Yumiao; Pan Haixian (IET Image Processing, 2023)
Abstract Though relatively good effect has been achieved by the image de‐blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain‐inspired image restoration model based on human attention and “fine vision” is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain‐inspired model consists of dual‐channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super‐resolution, as well as looking results by visual inspection.