PDE-based Specular Highlight Elimination

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Abstract

Dealing with reflections in images captured through glass would be real headache, as they can obscure the important stuff behind the glass and make the whole image look messy. This is a major problem in many computer vision tasks.Early studies reported that a popular way to tackle the challenge of removing reflections from [1] single images in deep learning. In this article, we take a deep dive into the research on this topic from 2015 to 2021, focusing on how deep learning is being used for [5] single-image reflection removal [4].We searched through a bunch of important online databases and libraries, like IEEE Xplore, Google Scholar, ScienceDirect, SpringerLink, and ACM Digital Library, to find relevant research papers. After carefully going through them, we picked out 25 papers [9] that fit the criteria for our review.We analyzed these papers to answer seven major questions about how deep learning and [3] neural networks are being used for [6] single-image reflection removal. This will hopefully give future researchers a good understanding of what's been done in this area and help them build on that knowledge.The review also highlights the important challenges that data scientists are facing in this area, and also some promising directions for future research. . And importantly, it provides a list of useful datasets that data scientists can use to benchmark their own deep learning techniques against other studies. Whether you're a researcher hungry for the next challenge or just someone who wants to understand how it all works, this review will equip you with the knowledge and inspiration to delve deeper into this fascinating field.
基于 PDE 的镜面高光消除
处理透过玻璃捕捉到的图像中的反光确实令人头疼,因为反光会遮住玻璃后面的重要内容,使整个图像看起来乱糟糟的。这是许多计算机视觉任务中的一个主要问题。早期的研究报告指出,在深度学习中,有一种流行的方法可以解决从[1]单张图像中去除反光的难题。在本文中,我们将深入探讨从 2015 年到 2021 年有关这一主题的研究,重点关注深度学习如何被用于[5]单幅图像的反光去除[4]。我们搜索了一堆重要的在线数据库和图书馆,如 IEEE Xplore、Google Scholar、ScienceDirect、SpringerLink 和 ACM Digital Library,以找到相关的研究论文。我们分析了这些论文,回答了关于深度学习和[3]神经网络如何用于[6]单图像反光去除的七个主要问题。希望这能让未来的研究人员很好地了解这一领域的研究成果,并帮助他们在此基础上更上一层楼。综述还强调了数据科学家在这一领域面临的重要挑战,以及未来研究的一些有前景的方向。.重要的是,它还提供了一份有用的数据集清单,数据科学家可以利用这些数据集将自己的深度学习技术与其他研究进行比较。无论您是渴望迎接下一个挑战的研究人员,还是只是想了解这一切是如何运作的人,这本综述都将为您提供深入这一迷人领域的知识和灵感。
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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