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Sensors 2016, 16(3), 288; doi:10.3390/s16030288

Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution

1,†
,
1,†,* , 2,†,* , 1
and
1
1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an 710071, China
2
Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 19 December 2015 / Revised: 27 January 2016 / Accepted: 10 February 2016 / Published: 26 February 2016
(This article belongs to the Special Issue Mobile Sensor Computing: Theory and Applications)
View Full-Text   |   Download PDF [2748 KB, uploaded 26 February 2016]   |  

Abstract

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception. View Full-Text
Keywords: visual sensor network; image super-resolution; image denoising; prior learning; EM algorithm visual sensor network; image super-resolution; image denoising; prior learning; EM algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yue, B.; Wang, S.; Liang, X.; Jiao, L.; Xu, C. Joint Prior Learning for Visual Sensor Network Noisy Image Super-Resolution. Sensors 2016, 16, 288.

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