改进的图像去雾与有雾图像的目标检测 Improved Techniques for Learning to Dehaze and Beyond: A Collective Study

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。

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.

https://github.com/guanlongzhao/dehaze

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