Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

08/05/2022
by   Yongsong Huang, et al.
0

In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves 31.90 of PSNR with a scale factor of 4 ×, which is 7.05 % higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.

READ FULL TEXT
research
06/13/2022

Learning a Degradation-Adaptive Network for Light Field Image Super-Resolution

Recent years have witnessed the great advances of deep neural networks (...
research
02/09/2021

Deep learning architectural designs for super-resolution of noisy images

Recent advances in deep learning have led to significant improvements in...
research
02/27/2021

Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions

Change detection, which aims to distinguish surface changes based on bi-...
research
03/02/2021

Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

In this paper, we propose a novel compressed image super resolution (CIS...
research
03/28/2022

An attention mechanism based convolutional network for satellite precipitation downscaling over China

Precipitation is a key part of hydrological circulation and is a sensiti...
research
08/10/2022

Learning Degradation Representations for Image Deblurring

In various learning-based image restoration tasks, such as image denoisi...
research
10/30/2022

Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

The License Plate Recognition (LPR) field has made impressive advances i...

Please sign up or login with your details

Forgot password? Click here to reset