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Abstract
Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.
Highlights
Single image super-resolution (SISR) aims at recovering a high-resolution (HR) image from a low-resolution (LR) one
To address the limitations of the simple mean squared error (MSE) loss, we focus on reference-based metrics with good performance. e visual information fidelity (VIF) [42], which is based on the amount of shared information between the reference and distorted images, can measure the visual information fidelity. e feature similarity index (FSIM) [43] can measure the dissimilarity between two images based on local phase congruency and gradient magnitude. e metric we chose, gradient magnitude similarity deviation (GMSD), is characterized by its simplicity and can still predict the perceptual image quality consistently with human vision system (HVS)
perceptual index (PI) was firstly introduced in 2018 perceptual image restoration and manipulation (PIRM) challenge, which was a competition for perceptual image super-resolution [53]. e challenge defines perceptual quality as the visual quality of the reconstructed image regardless of its similarity to the ground-truth image
Summary
Single image super-resolution (SISR) aims at recovering a high-resolution (HR) image from a low-resolution (LR) one. It is inherently ill posed since for one LR image, there exists multiple HR ones that could generate it. There have been many breakthroughs in addressing SISR, there is still one challenge: how to recover photorealistic results with more natural textures and less unpleasant artifacts. To this end, traditional methods and learning-based methods are proposed in succession. E mean squared error (MSE) loss is often used as the term of loss function when learning-based methods become popular. We attempt to alleviate the influence of MSE loss by crafting a novel loss term named quality loss and no longer aim to achieve state-of-the-art PSNR results
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