APPLICATION OF COMPRESSIVE SENSING TECHNIQUES FOR ADVANCED IMAGE PROCESSING AND DIGITAL IMAGE TRANSMISSION

Nenad Stefanović; Boban Sazdić-Jotić; Vladimir Orlić; Vladimir Mladenović; Stefan Ćirković

doi:10.5937/bnsr14-51559

Nenad Stefanović Center for Applied Mathematics and Electronics, Belgrade, Serbia https://orcid.org/0000-0002-0222-8311
Boban Sazdić-Jotić Military Technical Institute, Belgrade, Serbia https://orcid.org/0000-0001-7239-174X
Vladimir Orlić Vlatacom Institute, Belgrade, Serbia https://orcid.org/0000-0002-5153-5115
Vladimir Mladenović Faculty of Technical Sciences in Čačak, University of Kragujevac, Čačak, Serbia https://orcid.org/0000-0001-8530-2312
Stefan Ćirković Faculty of Technical Sciences in Čačak, University of Kragujevac, Čačak, Serbia https://orcid.org/0009-0004-6775-1543

DOI: https://doi.org/10.5937/bnsr14-51559

Keywords: Sensing, Reconstruction, Transmission, Sparsity, Algorithms

Abstract

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.

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