I. Introduction
Image super-resolution basically means improving or enhancing the resolution of an image. It is a problem to convert low-resolution image to high-resolution image or simply, to recover the image’s finer texture details. It has got attention over the time from many research communities as it has various practical applications [11, 12, 13]. There have been many Super Resolution (SR) algorithms such as SRCNN [43], introduced by Dong et al. [4, 5] which focus on the minimization of Mean Squared Error and thus, in tum, maximizes the Peak Signal to Noise Ratio [7], which is a wellknown metric for comparing the quality of images [8]. Though there have been many breakthroughs in this field with such models, and performance has been improved over time but such algorithms tend to produce results that lack perceptual clarity and fine texture details [44]. This is because MSE and PSNR aren’t able to do justification with the perceptually important features of an image as these metrics focus on the differences in pixels of the recovered image and ground truth image (generated super-resolved image and original highresolution image). The resultant images look so unrealistically smooth and devoid of any high-frequency detail that it fails in the subjective evaluation [2].