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### 图像压缩感知的特征域优化及自注意力增强神经网络重构算法

1. 华南理工大学电子与信息学院，广东 广州 510640
• 收稿日期:2022-02-07 修回日期:2022-04-11
• 作者简介:陈文俊 男，1997年出生，福建福州人.现为华南理工大学电子与信息学院硕士研究生.主要研究方向为图像/视频压缩感知.E‑mail：eecwjjun@mail.scut.edu.cn
杨春玲（通讯作者） 女，1970年出生，河南新乡人.现为华南理工大学电子与信息学院博士生导师．主要研究方向为图像/视频压缩编码、图像质量评价.
• 基金资助:
广东省自然科学基金(2019A1515011949)

### Feature-Space Optimization-Inspired and Self-Attention Enhanced Neural Network Reconstruction Algorithm for Image Compressive Sensing

CHEN Wen-jun, YANG Chun-ling

1. School of Electronic and Information Engineering，South China University of Technology，Guangzhou，Guangdong 510640，China
• Received:2022-02-07 Revised:2022-04-11

Abstract:

The existing optimization-inspired networks for image compressive sensing(ICS) implement information optimization and flow in the pixel domain following the traditional algorithms, which does not make full use of the information in the image feature maps extracted by the convolutional neural network. This paper proposes the idea of constructing information flow in the feature domain. A feature-space optimization-inspired network(FSOINet) is designed to implement this idea. Considering the small receptive field of the convolution operation, this paper introduces the self-attention module into FSOINet to efficiently utilize the non-local self-similarity of images to further improve the reconstruction quality, which is named FSOINet+. In addition, this paper proposes a training strategy that applies transfer learning to the ICS reconstruction network training for different sampling rates to improve the network learning efficiency and reconstruction quality. Experimental results show that the proposed method is superior to the existing state-of-the-art ICS methods in peak signal to noise ratio(PSNR), structural similarity index measure(SSIM) and the visual effect. Compared with OPINENet+ on the Set11 dataset, FSOINet and FSOINet+ have an average PSNR improvement of 1.04dB/1.27dB respectively.