Image Restoration Algorithm Based on Attention Mechanism and Feature Fusion

In response to the problems of semantic discontinuity and unclear texture in image restoration, an image restoration algorithm based on attention mechanism and feature fusion was proposed. The method first generates coarse repaired images by generating adversarial networks; Then, the coarse repaired image is used as input to establish a dual channel for repairing texture and structural details. The first channel combines low-level image information with deep semantic information through attention mechanism to repair missing texture details; The second channel is a generative network architecture with dilated convolutions, which captures image structural information through a large receptive field and increases the structural similarity of the repaired image. Finally, by fusing the dual channel feature information and processing the high-level features, it can improve the accuracy of structure generation and generate more natural texture details. The test results on the CelebA dataset show that the proposed method has improved in both structural similarity and texture clarity.