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Sensors 2014, 14(2), 3652-3674; doi:10.3390/s140203652

Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network

College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea
* Author to whom correspondence should be addressed.
Received: 8 January 2014 / Revised: 4 February 2014 / Accepted: 5 February 2014 / Published: 21 February 2014
(This article belongs to the Section Sensor Networks)


Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes.
Keywords: visual sensor; super-resolution; redundant dictionary; matching pursuit visual sensor; super-resolution; redundant dictionary; matching pursuit
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sajjad, M.; Mehmood, I.; Baik, S.W. Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network. Sensors 2014, 14, 3652-3674.

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