Adaptive Sampling for Image Compressed Sensing Based on Deep Learning

The compressed sensing (CS) theory has been applied to image compression successfully as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for network-based image compressed sensing by using our proposed adaptive sampling algorithm. We conduct content adaptive sampling to achieve a significant improvement. Experiments results indicate that our proposed framework outperforms the state-of-the-arts both in subjective and objective quality. An average of 1-6 dB improvement in peak signal to noise ratio (PSNR) is observed. Moreover, the proposed work reconstructs images with more details and less image blocking effects, leading to apparent visual improvement.