AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms  

作　　者：Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng 

机构地区：[1]Department of Geography and Anthropology,Louisiana State University,Baton Rouge,LA,70803,USA [2]School of Automation,University of Electronic Science and Technology of China,Chengdu,610054,China [3]College of Computer and Information Sciences,King Saud University,Riyadh,11574,Saudi Arabia [4]College of Resource and Environment Engineering,Guizhou University,Guiyang,550025,China [5]School of Geographical Sciences,Southwest University,Chongqing,400715,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第9期2315-2347,共33页工程与科学中的计算机建模（英文）

基　　金：Supported by Sichuan Science and Technology Program(2021YFQ0003,2023YFSY0026,2023YFH0004).

摘　　要：At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关 键 词：SUPER-RESOLUTION feature extraction dynamic convolution attention mechanism gate control 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]