  Single image super-resolution (SISR) is of great importance as a low-level
computer vision task. The fast development of Generative Adversarial Network
(GAN) based deep learning architectures realises an efficient and effective
SISR to boost the spatial resolution of natural images captured by digital
cameras. However, the SISR for medical images is still a very challenging
problem. This is due to (1) compared to natural images, in general, medical
images have lower signal to noise ratios, (2) GAN based models pre-trained on
natural images may synthesise unrealistic patterns in medical images which
could affect the clinical interpretation and diagnosis, and (3) the vanilla GAN
architecture may suffer from unstable training and collapse mode that can also
affect the SISR results. In this paper, we propose a novel lesion focused SR
(LFSR) method, which incorporates GAN to achieve perceptually realistic SISR
results for brain tumour MRI images. More importantly, we test and make
comparison using recently developed GAN variations, e.g., Wasserstein GAN
(WGAN) and WGAN with Gradient Penalty (WGAN-GP), and propose a novel
multi-scale GAN (MS-GAN), to achieve a more stabilised and efficient training
and improved perceptual quality of the super-resolved results. Based on both
quantitative evaluations and our designed mean opinion score, the proposed LFSR
coupled with MS-GAN has performed better in terms of both perceptual quality
and efficiency.
