Content-Adaptive Image Compressed Sensing Using Deep Learning

This paper proposes a framework of content-adaptive image compressed sensing using deep learning, which analyzes the image content and adaptively allocates samples for different image patches accordingly. Experimental results demonstrate that the proposed framework outperforms the state-of-the-arts both in subjective and objective quality, especially at low sampling rates. For example, when the sampling rate is 0.1, 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.