摘要
图像压缩感知是一种在欠采样条件下尽可能重构原始图像的技术。为解决大部分基于卷积神经网络(CNN)框架的图像压缩感知方法容易受到卷积感受野的限制、对全局信息的关注较少的问题,提出了基于Swin Transformer的图像压缩感知重构网络。网络使用卷积层对图像进行采样,然后使用自注意力机制和残差结构结合的残差Swin Transformer组(RSTG)结构来关注图像的细节。实验结果表明,基于Swin Transformer的图像压缩感知重构网络可以充分利用图像的先验信息,进一步提高图像压缩感知的重构精度,并获得比其他压缩感知方法更好的重构性能和视觉效果。
Image compressive sensing is a technology that reconstructs the original image as far as possible under the condition of under-sampling.Most of the image compressive sensing methods based on the framework of Convolutional Neural Network(CNN)are prone to be limited by the receptive field of convolution and pay less attention to global information.To solve the problem,an image reconstruction network using compressive sensing based on Swin Transformer is proposed.The network uses the convolutional layer for image sampling,and then uses the structure of Residual Swin Transformer Group(RSTG),which combines the self-attention mechanism with the residual structure,to focus on the details of the image.The experimental results show that the image reconstruction network using compressive sensing based on Swin Transformer can make full use of the prior information of the image,further improve the image reconstruction accuracy of compressive sensing,and obtain better reconstruction performance and visual effects than that of other compressive sensing methods.
作者
侯保军
田金鹏
张紫沁
HOU Baojun;TIAN Jinpeng;ZHANG Ziqin(School of Communication and Information Engineering,Shanghai University,Shanghai 200000,China)
出处
《电光与控制》
CSCD
北大核心
2023年第11期94-99,共6页
Electronics Optics & Control
基金
国家自然科学基金(61871261)
上海市科技攻关项目(19DZ1205802)。
关键词
压缩感知
图像重构
自注意力机制
残差
compressive sensing
image reconstruction
self-attention mechanism
residual
