Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality

Abstract

In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

“…On the other hand, generative models focus on the data distribution to discover the underlying features from large amounts of data in an unsupervised setting. Such models are able to generate new samples by learning the estimation of the joint probability distribution p (x,y) and predicting y [14] in contexts, such as image super-resolution [15,16], text-to-image generation [17,18], and image-to-image translation [19,20].…”

Unsupervised Hashing with Gradient Attention

“…Based on this hypothetical estimate, a pair-wise loss function was designed to retain semantic information. Generative adversarial networks (GAN) were widely used in various fields [33], Ghasedi Dizaji et al [34] used a shared parameter generator and discriminator to optimize the hash function in the adversarial. On the basis of GAN, Deng et al [35] constructed a semantic similarity matrix by using feature similarity and nearest neighbor similarity to guide the construction of hash codes.…”