{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:41:42Z","timestamp":1760218902101,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2014,2,21]],"date-time":"2014-02-21T00:00:00Z","timestamp":1392940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s140203652","type":"journal-article","created":{"date-parts":[[2014,2,21]],"date-time":"2014-02-21T11:55:11Z","timestamp":1392983711000},"page":"3652-3674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Sparse Representations-Based Super-Resolution of  Key-Frames Extracted from Frames-Sequences  Generated by a Visual Sensor Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Muhammad","family":"Sajjad","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irfan","family":"Mehmood","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung","family":"Baik","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, F., and Kumar, S. 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