Open AccessAdaptive Sampling for Image Compressed Sensing Based on Deep Learning
Author(s) -
Liqun Zhong,
Shuai Wan,
Lei Xie
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012016
Subject(s) - computer science , peak signal to noise ratio , compressed sensing , artificial intelligence , image (mathematics) , sampling (signal processing) , focus (optics) , computer vision , noise (video) , image quality , adaptive sampling , image compression , signal to noise ratio (imaging) , signal (programming language) , pattern recognition (psychology) , image processing , mathematics , statistics , telecommunications , physics , filter (signal processing) , monte carlo method , optics , programming language
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.