Classification Guided Deep Convolutional Network for Compressed Sensing

Compressed Sensing (CS) has been successfully applied to image compression in the past few years. However, there are still several challenges that restrict its applications in practice including large memory requirement and unsatisfactory reconstruction performance. To address these challenges, in this paper, we propose a classification guided deep convolutional network for image compressed sensing (CCSNet), which includes a sampling sub-network and a reconstruction sub-network. In the sampling sub-network, multiple convolutional layers are used to sample the original image, which significantly reduces the parameters of the sampling matrix while causes performance degradation moderately compared against existing convolution based sampling methods. In the reconstruction sub-network, a novel two-branch architecture is proposed to improve the adaptability of the model to various textures in natural images. The first branch, named the classification branch, is to classify the sampled measurements of the original image to one of the predefined textural classes. The second branch, named the reconstruction branch, consists of multiple sub-branches, which are responsible for reconstructing the original images belonging to the corresponding textural classes. By jointly utilizing two sub-networks, the entire network can be trained in the form of end-to-end metric with a joint loss function. Experimental results demonstrate that the proposed method provides a significant quality improvement in terms of PSNR compared against state-of-the-art methods.