计算机工程与科学2024，Vol.46Issue(8)：1433-1443,11.DOI:10.3969/j.issn.1007-130X.2024.08.012
多阶段特征蒸馏加权的轻量级图像超分辨率网络
A multi-stage feature distillation-weighted lightweight image super-resolution network
摘要
Abstract
To address the issues of insufficient receptive fields for extracting low-level features and the lack of reinforcement for local key features in lightweight networks,this paper proposed a multi-stage feature distillation-weighted lightweight image super-resolution network LMSWN.Firstly,a pyr-amid-like module is employed to expand the receptive field during shallow feature extraction,integrate feature information of different scales,and enrich the information flow of the network.Secondly,a multi-stage residual distillation-weighted module is designed to enhance the ability of square convolution to extract local key features,recover more detailed information,and improve reconstruction perform-ance.At the same time,the combination of channel separation and 1 × 1 convolution realizes gradual distillation of features,reducing the number of network parameters.Finally,two adaptive parameters are introduced to jointly learn the features of the two branches of the multi-stage residual distillation-weighted module,enhancing the attention to different levels of feature information and further enhan-cing the representation ability of the network.Experimental results show that the proposed network is fully validated on five benchmark datasets:Set 5,Set 14,BSDS 100,Urban 100,and Manga 109,and its performance exceeds the current mainstream lightweight network.关键词
图像超分辨率/轻量级/特征蒸馏/多尺度卷积Key words
image super-resolution/lightweight/feature distillation/multi-scale convolution分类
信息技术与安全科学引用本文复制引用
杨胜荣,车文刚,高盛祥,赵云莱..多阶段特征蒸馏加权的轻量级图像超分辨率网络[J].计算机工程与科学,2024,46(8):1433-1443,11.基金项目
国家自然科学基金(61972186,U21B2027) （61972186,U21B2027）
