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

此研究旨在探索基于最近发布的真实单张图像去雾 (RESIDE) 基准数据集的相关但重要的两项任务：(i) 将单张图像去雾作为低级图像恢复问题；以及 (ii) 雾化图像的高级视觉理解（例如目标检测）。在第一个任务中，我们研究了各种损失函数，并表明，感知损失显著提高了去雾性能。在第二个任务中，我们提供了多种解决方案，包括在去雾检测级联中使用先进模块和域自适应目标检测器。在两项任务中，我们的提议解决方案显著提高了性能。

学习去雾及进一步改进的技术：集体研究