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SSP-Net: A Siamese-Based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics; 2024, Vol. 21 Issue: 4 p681-691, 11p
- Publication Year :
- 2024
-
Abstract
- Recently, unpaired medical image enhancement is one of the important topics in medical research. Although deep learning-based methods have achieved remarkable success in medical image enhancement, such methods face the challenge of low-quality training sets and the lack of a large amount of data for paired training data. In this article, a dual input mechanism image enhancement method based on Siamese structure (SSP-Net) is proposed, which takes into account the structure of target highlight (texture enhancement) and background balance (consistent background contrast) from unpaired low-quality and high-quality medical images. Furthermore, the proposed method introduces the mechanism of the generative adversarial network to achieve structure-preserving enhancement by jointly iterating adversarial learning. Experiments comprehensively illustrate the performance in unpaired image enhancement of the proposed SSP-Net compared with other state-of-the-art techniques.
Details
- Language :
- English
- ISSN :
- 15455963 and 15579964
- Volume :
- 21
- Issue :
- 4
- Database :
- Supplemental Index
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Publication Type :
- Periodical
- Accession number :
- ejs67163939
- Full Text :
- https://doi.org/10.1109/TCBB.2023.3256709