- Thursday, Apr 2nd, 2026
| TITLE | Reconstruction of Images Based on Compressive Sensing |
|---|---|
| ABSTRACT | Modern imaging systems demand efficient storage, transmission, and acquisition due to the large size of image data. In medical imaging, such as MRI, long acquisition times reduce patient comfort. To address this, compressive sampling (CS) leverages the sparsity inherent in images to reduce the number of required measurements while maintaining quality. The key challenge lies in reconstructing high-quality images from limited and often noisy data—an inverse problem that lacks unique solutions. This work explores sparse representation techniques for image reconstruction, with a focus on greedy iterative algorithms. An extensive literature survey supports the proposed methodologies. Potential applications span biomedical imaging, satellite imaging, and other domains where data exhibits natural sparsity. |
| AUTHOR | Shashi Kiran. S, Apurva S, Gagandeep B S, Bhavana S, Arjun Vasista Rao K S Associate Professor, Dept. of ETE, JNN College of Engineering, Shivamogga, Karnataka, India Student, Dept. of ETE, JNN College of Engineering, Shivamogga, Karnataka, India |
| VOLUME | 13 |
| DOI | DOI:10.15680/IJARETY.2026.1302014 |
| 14_Reconstruction of Images Based on Compressive Sensing.pdf | |
| KEYWORDS | |
| References | [1] Shashi Kiran. S and Suresh K.V, "Image reconstruction through compressive sampling matching pursuit and curvelet transform", International journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 6, December 2023. [2] Miaowen Shi, Fan Zhang, Suwei Wang, Caiming Zhang, Xuemei Li, "Detail preserving image denoising with patch-based structure similarity via sparse representation and SVD," Computer Vision and Image Understanding, Volume 206, 103173. [3] Zhou, T., Li, C., Zeng, X. et al. "Sparse representation with enhanced nonlocal self-similarity for image denoising," Machine Vision and Applications, Volume 32, article number 110, 21 August 2021. [4] Leal N. Zurek E. Leal E. “Non-Local SVD Denoising of MRI Based on Sparse Representations,†Department of Systems Engineering, Universidad del Norte, Barranquilla 080001, Colombia, 10 March 2020. |
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