Adaptive Sampling for Image Compressed Sensing Based on Deep Learning. (May 2019)

• Record Type: Journal Article
• Authors: Zhong, Liqun; Wan, Shuai; Xie, Leyi
• Is Part Of: Journal of physics. Volume 1229(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1229/1/012016
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.