Image Compressed Sensing Using Multi-Scale Characteristic Residual Learning

Abstract

Deep network-based image compressed sensing (CS) methods have attracted much attention in recent years due to their low reconstruction complexity and high reconstruction quality. However, the existing methods usually use one or multiple convolution layer(s) consisting of convolutional kernels with the same size to extract image features in image sampling, which results in incomplete feature extraction. Besides, the existing models usually focus on the extraction of deep features in image reconstruction, while ignoring the influence of shallow features. To overcome these issues, this paper proposes a multi-scale characteristic residual learning network (dubbed MSCRLNet) for image CS. In this network, convolutional kernels with different sizes are used to capture multi-level spatial features in image sampling, and a multi-scale residual network with channel attention is used to speed up network convergence in image reconstruction. Experiments show that the proposed MSCRLNet outperforms many existing state-of-the-art methods.