SSP-Net: A Siamese-Based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement
Guoxia Xu, Hao Wang, Marius Pedersen, Meng Zhao, Hu Zhu
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.