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A Study on the Validity and Scope of Self-Similarity Property in Super-Resolution of Medical Images.
- Source :
-
Circuits, Systems & Signal Processing . Jul2024, Vol. 43 Issue 7, p4298-4318. 21p. - Publication Year :
- 2024
-
Abstract
- Super-resolution is an ill-posed inverse problem. It needs a self-similarity regularization term in its formulation to solve the issue. Researchers have studied self-similarity in natural images thoroughly and extended the results to medical data. This paper investigates the self-similarity property using a general affine model and the SSIM, PSNR, matrix rank, and mutual information measures. We study variations of self-similarity with image modality, inter/intra-resolution, tissue types, and acquisition phase. Based on our experiments, super-resolution algorithms that use self-similarity property achieve better results in CT images than MR data. Some organs, including the liver, have a stronger self-similarity compared to the brain and lung. The extension of our study prepares a comprehensive guide map to employ self-similarity properties in medical image processing algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0278081X
- Volume :
- 43
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Circuits, Systems & Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 178461749
- Full Text :
- https://doi.org/10.1007/s00034-024-02645-x