Deep learning based double branch restoration algorithm for old photos
Inspired by traditional image repair networks,a double branch image repair algorithm based on deep learning is pro-posed.The process of image repair is divided into two parts,one is the global branch of structural defect repair and the other is the local branch of unstructured defect repair.First,the residual module is used to build the overall network.Then,the compression excitation module Squeeze-and-Excitation(SE)is added to the residual module to construct the local branch.Finally,an attention mechanism Contextual Transformer(COT)is introduced to collect the intact global information in the image for structural defect repair and global branch construction.The experimental results show that the image repaired by this method has 0.02 SSIM gain and 0.23dB PSNR im-provement,which is superior to the visual quality and numerical indexes of other existing image restoration methods,and can effective-ly improve the restoration performance.
万方数据
维普
