In this paper we investigate into the problem of image quality assessment
(IQA) and enhancement via machine learning. This issue has long attracted a
wide range of attention in computational intelligence and image processing
communities, since, for many practical applications, e.g. object detection and
recognition, raw images are usually needed to be appropriately enhanced to
raise the visual quality (e.g. visibility and contrast). In fact, proper
enhancement can noticeably improve the quality of input images, even better
than originally captured images which are generally thought to be of the best
quality. In this work, we present two most important contributions. The first
contribution is to develop a new no-reference (NR) IQA model. Given an image,
our quality measure first extracts 17 features through analysis of contrast,
sharpness, brightness and more, and then yields a measre of visual quality
using a regression module, which is learned with big-data training samples that
are much bigger than the size of relevant image datasets. Results of
experiments on nine datasets validate the superiority and efficiency of our
blind metric compared with typical state-of-the-art full-, reduced- and
no-reference IQA methods. The second contribution is that a robust image
enhancement framework is established based on quality optimization. For an
input image, by the guidance of the proposed NR-IQA measure, we conduct
histogram modification to successively rectify image brightness and contrast to
a proper level. Thorough tests demonstrate that our framework can well enhance
natural images, low-contrast images, low-light images and dehazed images. The
source code will be released at
this https URL

本文研究了通过机器学习进行图像质量评估和增强的问题，并开发了一个新的无参考图像质量评估模型和一个基于质量优化的强大的图像增强框架。