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Improved Techniques for Learning to Dehaze and Beyond: A Collective Study

2018-06-30 16:52:33
Yu Liu, Guanlong Zhao, Boyuan Gong, Yang Li, Ritu Raj, Niraj Goel, Satya Kesav, Sandeep Gottimukkala, Zhangyang Wang, Wenqi Ren, Dacheng Tao

Abstract

This paper reviews the collective endeavors by the team of authors in exploring two interlinked important tasks, based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark: i) single image dehazing as a low-level image restoration problem; ii) high-level visual understanding (e.g., object detection) from hazy images. For the first task, the authors investigated a variety of loss functions, and found the perception-driven loss to improve dehazing performance very notably. For the second task, the authors came up with multiple solutions including using more advanced modules in the dehazing-detection cascade, as well as domain-adaptive object detectors. In both tasks, our proposed solutions are verified to significantly advance the state-of-the-art performance. The GitHub repository URL is: https://github.com/guanlongzhao/dehaze

Abstract (translated)

本文回顾了作者团队在最近发布的REalistic Single Image DEhazing(RESIDE)基准测试中探索两个相互关联的重要任务的集体努力:i)单图像去雾作为低级图像恢复问题; ii)来自模糊图像的高级视觉理解(例如,对象检测)。对于第一项任务,作者研究了各种损失函数,并发现感知驱动的损失非常显着地改善了除雾性能。对于第二项任务,作者提出了多种解决方案,包括在去雾检测级联中使用更高级的模块,以及域自适应对象检测器。在这两项任务中,我们提出的解决方案都经过验证,可以显着提升最先进的性能。 GitHub存储库URL是:https://github.com/guanlongzhao/dehaze

URL

https://arxiv.org/abs/1807.00202

PDF

https://arxiv.org/pdf/1807.00202.pdf


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