Perceptual cGAN for MRI Super-resolution

Nasser, Sahar Almahfouz; Shamsi, Saqib; Bundele, Valay; Garg, Bhavesh; Sethi, Amit

doi:10.1109/embc48229.2022.9871832

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Liu et al [35] proposed CCWGAN, utilizing residual dense blocks to generate high-quality remote sensing images effectively. In the realm of MRI super-resolution, Nasser et al [36] successfully elevated the performance of isotropic and anisotropic MRI super-resolution by incorporating perceptual loss and conditioning on low-resolution MRI images. Hanano et al [37] achieved the improved generation of facial expression images by enhancing cGAN in combination with a self-supervised guided encoder.…”

Section: Gan-based Methodsmentioning

confidence: 99%

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Kong,

Liu

2023

Remote Sensing

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Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.

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Section: Gan-based Methodsmentioning

confidence: 99%

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Kong,

Liu

2023

Remote Sensing

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“…SISR is a challenging task that aims to reconstruct an HR image from an LR image. This task has many applications in various fields, such as satellite imagery [29], monitoring equipment [30], remote sensing images [31], medical imaging [32], and so on. However, SISR is an ill-posed problem because one LR image can correspond to multiple HR images.…”

Section: Single-scale Deep Back-projection Networkmentioning

confidence: 99%

A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models

2023

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Face verification and recognition are important tasks that have made great progress in recent years. However, recognizing low-resolution faces from small images is still a difficult problem. In this paper, we advocate using diffusion models (DMs) to enhance face resolution and improve their quality for various downstream applications. Most existing DMs for super-resolution use U-Net as their backbone network, which only exploits multi-scale features along the spatial dimension. These approaches result in a slow convergence of corresponding DMs and the inability to capture complex details and fine textures. To address this issue, we propose a novel conditional generative model based on DMs called BPSR3, which replaces the U-Net in super-resolution via repeated refinement (SR3) with a multi-scale deep back-projection network structure. BPSR3 can extract richer features not only in depth but also in breadth. This helps to effectively refine the image quality at different scales. The experimental results on facial datasets show that BPSR3 significantly improved both convergence speed and reconstruction performance. BPSR3 has about 1/4 of the parameters of SR3 but achieves a 50.1% improvement in PSNR, a 19.8% improvement in SSIM, and a 15.4% reduction in FID. Our contribution lies in achieving less time and space consumption and better reconstruction results. In addition, we propose an idea of enhancing the performance of DMs by replacing the U-Net with a better network.

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