Application of compressive sensing techniques for advanced image processing and digital image transmission
The field of compressive sensing (CS) has emerged as a transformative approach in the acquisition and processing of high-dimensional data. This paper presents a comprehensive study on the application of compressive sensing techniques to advanced image processing and digital image transmission. By le...
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| Format: | Article |
| Language: | English |
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University of Priština - Faculty of Natural Sciences and Mathematics, Kosovska Mitrovica
2024-01-01
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| Series: | Bulletin of Natural Sciences Research |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/2738-0971/2024/2738-09712401050S.pdf |
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| _version_ | 1841554282680156160 |
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| author | Stefanović Nenad Sazdić-Jotić Boban Orlić Vladimir Mladenović Vladimir Ćirković Stefan |
| author_facet | Stefanović Nenad Sazdić-Jotić Boban Orlić Vladimir Mladenović Vladimir Ćirković Stefan |
| author_sort | Stefanović Nenad |
| collection | DOAJ |
| description | The field of compressive sensing (CS) has emerged as a transformative approach in the acquisition and processing of high-dimensional data. This paper presents a comprehensive study on the application of compressive sensing techniques to advanced image processing and digital image transmission. By leveraging the inherent sparsity in natural images, CS allows for significant reductions in the amount of data required for accurate reconstruction, thereby overcoming the limitations imposed by the traditional Shannon-Nyquist sampling theorem. We explore the theoretical foundations of CS, including the principles of sparsity and incoherence, and provide a detailed overview of the Orthogonal Matching Pursuit (OMP) algorithm, a prominent greedy algorithm used for sparse signal recovery. Experimental results demonstrate the efficacy of CS in improving image reconstruction quality, as evidenced by enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, we discuss the practical implementation of CS in single-pixel cameras and its potential impact on future imaging technologies. The findings suggest that CS offers a robust framework for efficient image acquisition and processing, making it a valuable tool for various applications in multimedia, medical imaging, and remote sensing. |
| format | Article |
| id | doaj-art-c22f5d4282a5456d900bdc99b99e9b9f |
| institution | Kabale University |
| issn | 2738-1013 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Priština - Faculty of Natural Sciences and Mathematics, Kosovska Mitrovica |
| record_format | Article |
| series | Bulletin of Natural Sciences Research |
| spelling | doaj-art-c22f5d4282a5456d900bdc99b99e9b9f2025-01-08T15:22:20ZengUniversity of Priština - Faculty of Natural Sciences and Mathematics, Kosovska MitrovicaBulletin of Natural Sciences Research2738-10132024-01-01141-2505910.5937/bnsr14-515592738-09712401050SApplication of compressive sensing techniques for advanced image processing and digital image transmissionStefanović Nenad0https://orcid.org/0000-0002-0222-8311Sazdić-Jotić Boban1https://orcid.org/0000-0001-7239-174XOrlić Vladimir2https://orcid.org/0000-0002-5153-5115Mladenović Vladimir3https://orcid.org/0000-0001-8530-2312Ćirković Stefan4Center for Applied Mathematics and Electronics, Belgrade, SerbiaMilitary Technical Institute, Belgrade, SerbiaVlatacom Institute, Belgrade, SerbiaUniversity of Kragujevac, Faculty of Technical Sciences in Čačak, Čačak, SerbiaUniversity of Kragujevac, Faculty of Technical Sciences in Čačak, Čačak, SerbiaThe field of compressive sensing (CS) has emerged as a transformative approach in the acquisition and processing of high-dimensional data. This paper presents a comprehensive study on the application of compressive sensing techniques to advanced image processing and digital image transmission. By leveraging the inherent sparsity in natural images, CS allows for significant reductions in the amount of data required for accurate reconstruction, thereby overcoming the limitations imposed by the traditional Shannon-Nyquist sampling theorem. We explore the theoretical foundations of CS, including the principles of sparsity and incoherence, and provide a detailed overview of the Orthogonal Matching Pursuit (OMP) algorithm, a prominent greedy algorithm used for sparse signal recovery. Experimental results demonstrate the efficacy of CS in improving image reconstruction quality, as evidenced by enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Additionally, we discuss the practical implementation of CS in single-pixel cameras and its potential impact on future imaging technologies. The findings suggest that CS offers a robust framework for efficient image acquisition and processing, making it a valuable tool for various applications in multimedia, medical imaging, and remote sensing.https://scindeks-clanci.ceon.rs/data/pdf/2738-0971/2024/2738-09712401050S.pdfsensingreconstructiontransmissionsparsityalgorithms |
| spellingShingle | Stefanović Nenad Sazdić-Jotić Boban Orlić Vladimir Mladenović Vladimir Ćirković Stefan Application of compressive sensing techniques for advanced image processing and digital image transmission Bulletin of Natural Sciences Research sensing reconstruction transmission sparsity algorithms |
| title | Application of compressive sensing techniques for advanced image processing and digital image transmission |
| title_full | Application of compressive sensing techniques for advanced image processing and digital image transmission |
| title_fullStr | Application of compressive sensing techniques for advanced image processing and digital image transmission |
| title_full_unstemmed | Application of compressive sensing techniques for advanced image processing and digital image transmission |
| title_short | Application of compressive sensing techniques for advanced image processing and digital image transmission |
| title_sort | application of compressive sensing techniques for advanced image processing and digital image transmission |
| topic | sensing reconstruction transmission sparsity algorithms |
| url | https://scindeks-clanci.ceon.rs/data/pdf/2738-0971/2024/2738-09712401050S.pdf |
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