Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Yu Liu, Guanlong Zhao, Boyuan Gong, Yang Li, Ritu Raj, Niraj Goel,, Satya Kesav, Sandeep Gottimukkala, Zhangyang Wang, Wenqi Ren, Dacheng Tao

TL;DR
This paper presents improved techniques for single image dehazing and high-level visual understanding of hazy images, demonstrating significant performance gains through novel loss functions and advanced detection modules.
Contribution
It introduces perception-driven loss functions for dehazing and domain-adaptive detection modules for better understanding of hazy images, advancing the state-of-the-art in both tasks.
Findings
Perception-driven loss enhances dehazing quality
Advanced modules improve detection in hazy conditions
Proposed methods outperform existing approaches
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
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Enhancement Techniques · Video Surveillance and Tracking Methods · Advanced Neural Network Applications
