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
转载请注明：《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》
