A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Marco Klaiber, Jonas Klopfer
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引用次数: 0

Abstract

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our results
基于SOTA机器学习的计算机视觉图像增强方法的系统文献综述
图像增强作为一个面向问题的过程,优化视觉外观,为自动图像处理技术提供更容易处理的输入,尽管图像采集取得了进步,但它将始终是计算机视觉的伴侣,其相关性也在不断增长。对于我们的系统文献综述,我们考虑了基于机器学习的计算机视觉图像增强方法的最新技术的主要同行评审期刊和会议论文。我们描述了与我们的工作相关的图像增强方法,并介绍了所使用的机器学习模型。然后,我们提供了不同应用领域的全面概述,并根据我们的结果制定了基于机器学习的计算机视觉图像增强方法的未来科学工作的研究差距
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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