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

How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach