An Image Restoration Model of Adversarial Auto-encoder Based on Attention Mechanism
HUANG Ziyu
QIAN Chonghui
HUANG Hengjun
Abstract：In order to solve the problems of artifacts and inconsistent details in image inpainting,an adversarial auto-encoder based on attention mechanism(AAEA)image restoration model was proposed.Based on the universal encoder model,the channel similarity fusion module(CSFM)was constructed by introducing channel attention at the generator jump connection,which enriched the feature relationship between channels.In the decoder network,a location fusion model(LFM)was constructed by combining spatial attention with location coding to enhance the expression of boundary location information.The results of ablation experiments showed that after introducing CSFM and LFM,the performance of the model is effectively improved,and the accuracy reached 0.9808 with the threshold of 1.253.The AAEA model could better deal with complex image restoration tasks,effectively correct disordered textures,and obtain clear restoration results in the edge region of the mask,which is of great significance for the development of mural restoration and computer vision and other image restoration fields.
Keywords：attention mechanismadversarial auto-encoderimage restorationdeep learningarea filling
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 81-85,91 )