[2603.00204] Optimisation of SOUP-GAN and CSR-GAN for High Resolution MR Images Reconstruction

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.00204 (eess) [Submitted on 27 Feb 2026] Title:Optimisation of SOUP-GAN and CSR-GAN for High Resolution MR Images Reconstruction Authors:Muneeba Rashid, Hina Shakir, Humaira Mehwish, Asarim Amir, Reema Qaiser Khan View a PDF of the paper titled Optimisation of SOUP-GAN and CSR-GAN for High Resolution MR Images Reconstruction, by Muneeba Rashid and 4 other authors View PDF Abstract:Magnetic Resonance (MR) imaging is a diagnostic tool used in modern medicine; however, its output can be affected by motion artefacts and may be limited by equipment. This research focuses on MRI image quality enhancement using two efficient Generative Adversarial Networks (GANs) models: SOUP-GAN and CSR-GAN. In both models, meaningful architectural modifications were introduced. The generator and discriminator of each were further deepened by adding convolutional layers and were enhanced in filter sizes as well. The LeakyReLU activation function was used to improve gradient flow, and hyperparameter tuning strategies were applied, including a reduced learning rate and an optimal batch size. Moreover, spectral normalisation was proposed to address mode collapse and improve training stability. The experiment shows that CSR-GAN has better performance in reconstructing the image with higher frequency details and reducing noise compared to other methods, with an optimised PSNR of 34.6 and SSIM of 0.89. However, SOUP-GAN ...