Deep Objective Quality Assessment Driven Single Image Super-Resolution

Bo Yan

doi:10.1109/TMM.2019.2914883

Return

Deep Objective Quality Assessment Driven Single Image Super-Resolution

2019-11-01

PRE

DOI：10.1109/TMM.2019.2914883

Original

Original request for help

Save

Abstract

En 中文

Single-image super-resolution (SISR) is a classic problem in the image processing community, which aims at generating a high-resolution image from a low-resolution one. In recent years, deep learning based SISR methods emerged and achieved a performance leap than previous methods. However, because the evaluation metrics of SISR methods is peak signal-to-noise ratio (PSNR), previous methods usually choose L2-norm as the loss function. This leads to a significant improvement in the final PSNR value but little improvement in perceptual quality. In this paper, in order to achieve better results in both perceptual quality and PSNR values, we propose an objective quality assessment driven SISR method. First, we propose a novel full-reference image quality assessment approach for SISR and employ it as a loss function, namely super-resolution image quality assessment (SR-IQA) loss. Then, we combine SR-IQA loss with L2-norm to guide our proposed SISR method to achieve better results. Besides that, our proposed SISR method consists of several proposed highway units. Furthermore, in order to verify the generalization ability of our new kind of loss function, we integrate SR-IQA loss to generative adversarial networks based SR method and achieve better perceptual quality. Experimental results prove that our proposed SISR method achieves better performance than other methods both qualitatively and quantitatively in most of the cases.

Keywords:

Feature extraction

Image resolution

Image quality

Quality assessment

Signal resolution

Deep learning

Measurement

Single image super-resolution

full-reference quality assessment

generative adversarial networks

image enhancement

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.