基于特征相似性分析的轻量级图像超分辨率重建

doi:10.11896/jsjkx.250100057

Abstract

Abstract: Lightweight image super-resolution(SR) networks based on Transformer have achieved promising results.However,most research efforts have focused on designing lightweight architectures while neglecting the analysis of structural redundancy within the SR networks.To address this,the feature similarity-based model design approach is proposed,which compresses attention groups with high feature similarity while retaining those with low similarity,effectively reducing redundancy within SR network.Furthermore,a novel feature extraction module integrating the frequency and spatial domains is proposed.By separately performing localized frequency domain and spatial domain feature extraction,the model can leverage a broader range of input pixels with positive influences,effectively enhancing its capability to restore fine texture details.By applying the proposed method to baseline models,comparative results on multiple benchmark datasets demonstrate that the proposed approach achieves superior visual perceptual quality and reconstruction performance while maintaining low computational complexity.

Key words: Image super-resolution, Lightweight model, Centered kernel alignment, Feature similarity, Frequency domain

CLC Number:

TP391

LIU Xingpeng, XUE Yiming, LIN Yuyang, LI Yan, PENG Wanli. Lightweight Image Super-resolution Reconstruction Based on Feature Similarity Analysis[J].Computer Science, 2025, 52(11A): 250100057-8.

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Lightweight Image Super-resolution Reconstruction Based on Feature Similarity Analysis

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