湖南大学学报（自然科学版）2024，Vol.51Issue(8)：34-46,13.DOI:10.16339/j.cnki.hdxbzkb.2024276
基于滤波器剪枝的多尺度压缩感知图像重构
Multi-scale Compressed Sensing Image Reconstruction Based on Filter Pruning
摘要
Abstract
To address the issue of texture detail blurring in single-scale sampled compressed sensing image reconstruction at low sampling rates and achieve a lightweight reconstruction network,this paper proposes a filter pruning based multi-scale compressed sensing image reconstruction network.In the sampling phase,the image is linearly decomposed by convolution,and then fused with the input image and different scale decomposition features to obtain the compressed sensing measurements.In the reconstruction phase,a coordinate attention based multi-scale dilated residual module is designed,which incorporates positional information into channel attention to enhance the feature learning ability of the network.Moreover,by calculating the entropy of the feature map to judge the importance of the filters,the less important filters is pruned to achieve the purpose of compressing the model.Training and testing are carried out on datasets such as DIV2K,Set5,BSDS68 and Urban100.The experimental results show that the algorithm proposed improves the Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM),and image visual effects.For instance,with a sampling rate of 4%and a test set of Set14,the proposed algorithm improves the PSNR of the reconstructed image by 4.17 dB and 2.39 dB,respectively,compared with CSNet+and FSOINet,resulting in clearer texture details.Under the premise of slightly reducing the reconstruction effect,a lighter model was obtained,which improved the reconstruction speed.关键词
压缩感知/图像重构/多尺度融合/坐标注意力/滤波器剪枝Key words
compressed sensing/image reconstruction/multi-scale fusion/coordinate attention/filter pruning分类
计算机与自动化引用本文复制引用
刘玉红,姜启,谈丽娟,杨恒..基于滤波器剪枝的多尺度压缩感知图像重构[J].湖南大学学报（自然科学版）,2024,51(8):34-46,13.基金项目
国家自然科学基金资助项目(62161016,61661025),National Natural Science Foundation of China(62161016,61661025) （62161016,61661025）
