RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising

Abstract

A common problem in the field of deep-learning-based low-level vision medical images is that most of the research is based on single task learning (STL), which is dedicated to solving one of the situations of low resolution or high noise. Our motivation is to design a model that can perform both SR and DN tasks simultaneously, in order to cope with the actual situation of low resolution and high noise in low-level vision medical images. By improving the existing single image super-resolution (SISR) network and introducing the idea of multi-task learning (MTL), we propose an end-to-end lightweight MTL generative adversarial network (GAN) based network using residual-in-residual-blocks (RIR-Blocks) for feature extraction, RIRGAN, which can concurrently accomplish super-resolution (SR) and denoising (DN) tasks. The generator in RIRGAN is composed of several residual groups with a long skip connection (LSC), which can help form a very deep network and enable the network to focus on learning high-frequency (HF) information. The introduction of a discriminator based on relativistic average discriminator (RaD) greatly improves the discriminator’s ability and makes the generated image have more realistic details. Meanwhile, the use of hybrid loss function not only ensures that RIRGAN has the ability of MTL, but also enables RIRGAN to give a more balanced attention between quantitative evaluation of metrics and qualitative evaluation of human vision. The experimental results show that the quality of the restoration image of RIRGAN is superior to the SR and DN methods based on STL in both subjective perception and objective evaluation metrics when processing medical images with low-level vision. Our RIRGAN is more in line with the practical requirements of medical practice.