A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives
A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives
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单幅图像去雨的综合基准分析：当前挑战和未来展望
DOI：
10.1007/s11263-020-01416-w
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发表时间：
2021-01
影响因子：
19.5
通讯作者：
Xiaochun Cao
中科院分区：
文献类型：
--
作者：
Siyuan Li;Wenqi Ren;Feng Wang;Iago Breno Araujo;Eric K. Tokuda;Roberto Hirata Junior;Roberto M. Cesar-Jr;Zhangyang Wang;Xiaochun Cao
图像去伪能力是自动驾驶和户外监控系统中智能决策的重要组成部分。图像去噪的目的是从雨天拍摄的降质图像中恢复干净的场景。虽然最近已经提出了许多单图像去盲算法，这些算法主要是使用某种类型的合成图像，假设一个特定的雨模型，加上一些真实的图像进行评估。目前还不清楚这些算法将如何在“野外”获得的下雨图像上执行，以及我们如何衡量该领域的进展。本文旨在弥合这一差距。我们提出了一个全面的研究和评估现有的单图像deraining算法，使用一个新的大规模的基准组成的合成和真实世界的各种雨类型的下雨图像。该数据集突出了不同的降雨模型（雨条纹，雨滴，雨和雾），以及各种各样的评估标准（完整和无参考客观，主观和特定于任务）。我们进一步提供了一套全面的标准deraining算法评估，包括完整和无参考指标，主观评价，和新的任务驱动的评价。拟议的基准是伴随着广泛的实验结果，有利于国家的最先进的定量评估。我们的评估和分析表明，在合成的雨图像上可实现的性能与实际世界图像的实际需求之间存在差距。我们发现，尽管取得了许多进展，但图像脱轨仍然是一个很大程度上开放的问题。本文最后总结了我们的一般观察，确定开放的研究挑战，并指出未来的发展方向。我们的代码和数据集可在 http://uee.me/ddQsw .
The capability of image deraining is a highly desirable component of intelligent decision-making in autonomous driving and outdoor surveillance systems. Image deraining aims to restore the clean scene from the degraded image captured in a rainy day. Although numerous single image deraining algorithms have been recently proposed, these algorithms are mainly evaluated using certain type of synthetic images, assuming a specific rain model, plus a few real images. It remains unclear how these algorithms would perform on rainy images acquired “in the wild” and how we could gauge the progress in the field. This paper aims to bridge this gap. We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images of various rain types. This dataset highlights diverse rain models (rain streak, rain drop, rain and mist), as well as a rich variety of evaluation criteria (full- and no-reference objective, subjective, and task-specific). We further provide a comprehensive suite of criteria for deraining algorithm evaluation, including full- and no-reference metrics, subjective evaluation, and the novel task-driven evaluation. The proposed benchmark is accompanied with extensive experimental results that facilitate the assessment of the state-of-the-arts on a quantitative basis. Our evaluation and analysis indicate the gap between the achievable performance on synthetic rainy images and the practical demand on real-world images. We show that, despite many advances, image deraining is still a largely open problem. The paper is concluded by summarizing our general observations, identifying open research challenges and pointing out future directions. Our code and dataset is publicly available at http://uee.me/ddQsw .
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DOI：
10.1609/aaai.v34i07.6865
发表时间：
2019-11
期刊：
ArXiv
影响因子：
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通讯作者：
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