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

June 30, 2018 · Entered Twilight · 🏛 arXiv.org

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Authors Yu Liu, Guanlong Zhao, Boyuan Gong, Yang Li, Ritu Raj, Niraj Goel, Satya Kesav, Sandeep Gottimukkala, Zhangyang Wang, Wenqi Ren, Dacheng Tao arXiv ID 1807.00202 Category cs.CV: Computer Vision Cross-listed cs.LG Citations 45 Venue arXiv.org Repository https://github.com/guanlongzhao/dehaze ⭐ 47 Last Checked 1 month ago

Abstract

Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e.g., object detection) of hazy images. For the first task, we investigated a variety of loss functions and show that perception-driven loss significantly improves dehazing performance. In the second task, we provide multiple solutions including using advanced modules in the dehazing-detection cascade and domain-adaptive object detectors. In both tasks, our proposed solutions significantly improve performance. GitHub repository URL is: https://github.com/guanlongzhao/dehaze