Multi-Scale Deep Networks for Image Compressed Sensing

As a successful deep model applied in image compressed sensing, the Compressed Sensing Network (CSNet) has demonstrated superior performance to the previous handcrafted models in both running speed and reconstruction quality. However, CSNet trains different models for different sampling rates that hinders it from practical usage since too many models need to store. In this paper, we propose multi-scale deep network for image compressed sensing. We still use a sampling network to learn the sampling operator and implement the compressed sampling process. Given the compressed measurements, the reconstruction network directly maps them to the desired reconstructed images. There are three main differences in comparison with CSNet. Firstly, this paper proposes to use an unified deep reconstruction network for all sampling rates that decreases large amount of storage requirements. Secondly, we redesign a better deep reconstruction network using the popular residual learning technology. Finally, we investigate an image local smooth prior based loss function to enhance image structural information. Extensive experimental results show that the proposed multi-scale deep network based image compressed sensing method outperforms many other state-of-the-art methods.