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

Abstract

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 superresolution 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.