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

CHEN Wen-jun; YANG Chun-ling

doi:10.12263/DZXB.20220155

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

PAPERS | 更新时间：2025-12-08

- Feature-Space Optimization-Inspired and Self-Attention Enhanced Neural Network Reconstruction Algorithm for Image Compressive Sensing
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2629-2637(2022)
- 作者机构：
  
  华南理工大学电子与信息学院，广东广州 510640
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220155
  CLC： TN919.8
- Received：07 February 2022，
  
  Revised：2022-04-11，
  
  Published：25 November 2022
- 稿件说明：
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陈文俊,杨春玲.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[J].电子学报,2022,50(11):2629-2637.

CHEN Wen-jun,YANG Chun-ling.Feature-Space Optimization-Inspired and Self-Attention Enhanced Neural Network Reconstruction Algorithm for Image Compressive Sensing[J].ACTA ELECTRONICA SINICA,2022,50(11):2629-2637.
陈文俊,杨春玲.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[J].电子学报,2022,50(11):2629-2637. DOI： 10.12263/DZXB.20220155.

CHEN Wen-jun,YANG Chun-ling.Feature-Space Optimization-Inspired and Self-Attention Enhanced Neural Network Reconstruction Algorithm for Image Compressive Sensing[J].ACTA ELECTRONICA SINICA,2022,50(11):2629-2637. DOI： 10.12263/DZXB.20220155.

摘要

现有的图像压缩感知（Image Compressive Sensing， ICS）优化启发网络沿用了传统算法的像素域优化思想，构建了像素域的图像信息流动通道，而没有充分利用卷积神经网络所提取的图像特征中的信息.对此，本文提出了在特征域构建信息流的思想，并设计了一种特征域优化启发ICS网络（Feature-Space Optimization-Inspired Network， FSOINet）以实现该思想.考虑到卷积操作感受野较小，本文通过将自注意力模块引入FSOINet以更高效地利用图像非局部自相似性，进一步提高重构质量，我们将其命名为FSOINet

.此外，本文还提出把迁移学习策略应用于不同采样率图像压缩感知重构网络训练中，提高网络学习效率与重构质量.仿真实验表明，本文所提出的网络在峰值信噪比（Peak Signal to Noise Ratio， PSNR）、结构相似性（Structural Similarity Index Measure， SSIM）与视觉效果上都优于现有的最优ICS重构方法，FSOINet与FSOINet

在Set11数据集上与OPINENet

相比重构图像PSNR分别平均提升了1.04dB和1.27dB.

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.

关键词

Keywords

references

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