哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(4)：399-409,11.
基于小波-残差注意力的超分辨率重建网络
Cross super-resolution reconstruction network based on wavelet residual attention fusion
摘要
Abstract
To address the issues of insufficient utilization of frequency domain features and low efficiency in the fusion of global and local features in existing super-resolution methods,a wavelet-residual attention network for super-resolution(WRANet)had been proposed.A cross-band feature extraction module(FFB)had been designed,and a Wavelet Attention Convolution(WAConv)module had been proposed,which had employed a two-layer wavelet operation to obtain multi-scale frequency domain features.High-frequency features had been extracted and an extremely large receptive field had been obtained through convolution and a spatial-channel dual-branch attention mechanism(DAB).Residual attention(RAB)had been utilized to focus on high-frequency details.A cross-layer dense connection strategy had been established to dynamically fuse shallow texture and deep semantic features via 1×1 convolution.A feature aggregation module(FAM)had been employed to merge fine-grained local features and coarse-grained global features.Experiments had shown that the proposed method had achieved higher objective metrics compared to other methods,and the visual results had demonstrated that the images reconstructed by this method contained rich texture details.关键词
超分辨率重构/残差注意力/小波变换/注意力机制/卷积神经网络/密集连接Key words
super-resolution reconstruction/residual attention/wavelet transformer/attention mechanism/convolutional neural network/dense connections分类
信息技术与安全科学引用本文复制引用
许光宇,吴敏..基于小波-残差注意力的超分辨率重建网络[J].哈尔滨商业大学学报（自然科学版）,2026,42(4):399-409,11.基金项目
国家自然科学基金(61471004) （61471004）
安徽理工大学博士专项基金(ZX942) （ZX942）
