基于残差特征聚合的图像压缩感知注意力神经网络

Image Compressed Sensing Attention Neural Network Based on Residual Feature Aggregation

基于深度学习的图像压缩感知方法由于其具有强大的学习能力和快速的处理速度受到了广泛关注。随着卷积神经网络深度的增加,现有使用神经网络的图像重构方法未充分利用网络中的残差特征。为了解决这一问题,通过联合优化采样和逆重构过程,提出了一个基于残差特征聚合的图像压缩感知注意力神经网络框架。首先,构建了块压缩感知采样子网络和初始重构子网络,以自适应地学习测量矩阵并生成初步的重构图像。然后引入残差学习与空间注意力机制,构建残差特征聚合注意力重构子网络使残差特征更加集中于关键的空间内容,以进一步提高重构图像质量。实验结果表明,所提网络在重构时间相当的情况下优于现有的图像压缩感知重构算法,获得了更加优良的图像压缩感知重构质量。具体地,在采样率为0.10的情况下,使用11幅图像进行测试,与其他基于深度学习的方法相比,其平均峰值信噪比提高了0.34~6.18 dB。

Deep learning-based image compressive sensing has received extensive attention due to its powerful learning ability and fast processing speed.With the increase in the depth of convolutional neural networks,the existing image reconstruction methods using neural networks do not fully utilize the residual features in the network.In order to solve this problem,this paper proposes a compressed sensing attention neural network framework based on residual feature aggregation(RFA2CSNet)by jointly optimizing the sampling and inverse reconstruction processes.First,the block compressed sensing sampling sub-network and the initial reconstruction sub-network are constructed to adaptively learn the measurement matrix and generate the initial reconstruction image.Then the residual learning and spatial attention mechanisms are introduced to construct the residual feature aggregation attention reconstruction sub-network to make the residual feature more focused on the key spatial content,so as to further improve the reconstructed image quality.Experimental results show that the proposed network is superior to the existing image compressed sensing reconstruction algorithm in the case of comparable reconstruction time,and obtains better image compressive sensing reconstruction quality.Specifically,using 11 images for testing with a sampling rate of 0.10,the average peak signal-to-noise ratio increases by 0.34~6.18 dB compared with other deep learning-based methods.