Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network
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 resolut...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/2/3652 |
| _version_ | 1811277928799928320 |
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| author | Muhammad Sajjad Irfan Mehmood Sung Wook Baik |
| author_facet | Muhammad Sajjad Irfan Mehmood Sung Wook Baik |
| author_sort | Muhammad Sajjad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:26:39Z |
| format | Article |
| id | doaj.art-5e84f2c6ea1d49008524429424f1d154 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:26:39Z |
| publishDate | 2014-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5e84f2c6ea1d49008524429424f1d1542022-12-22T03:10:36ZengMDPI AGSensors1424-82202014-02-011423652367410.3390/s140203652s140203652Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor NetworkMuhammad Sajjad0Irfan Mehmood1Sung Wook Baik2College of Electronics and Information Engineering, Sejong University, Seoul 143-747, KoreaCollege of Electronics and Information Engineering, Sejong University, Seoul 143-747, KoreaCollege of Electronics and Information Engineering, Sejong University, Seoul 143-747, KoreaVisual 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.http://www.mdpi.com/1424-8220/14/2/3652visual sensorsuper-resolutionredundant dictionarymatching pursuit |
| spellingShingle | Muhammad Sajjad Irfan Mehmood Sung Wook Baik Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network Sensors visual sensor super-resolution redundant dictionary matching pursuit |
| title | Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network |
| title_full | Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network |
| title_fullStr | Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network |
| title_full_unstemmed | Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network |
| title_short | Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network |
| title_sort | sparse representations based super resolution of key frames extracted from frames sequences generated by a visual sensor network |
| topic | visual sensor super-resolution redundant dictionary matching pursuit |
| url | http://www.mdpi.com/1424-8220/14/2/3652 |
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