Abstract
Haze, which occurs as a result of the scattering of light in the atmosphere by small particles, diminishes the visibility of scene objects, inflicting important image applications such as object detection. To address the problem, this paper introduces a new physics-based end-to-end deep learning approach to haze mitigation in outdoor scenes, including those in airborne images. The proposed model named DenseCL is designed with dense blocks and adopts a contrastive loss function as an additional regularization. The model also maintains the cycle consistency by remapping the dehazed outputs into a hazy image using the physics-based light scattering function. DenseCL has been trained with publicly available outdoor images and demonstrates outstanding performance on outdoor, indoor, and remotely sensed nonhomogeneous haze satellite images.
Original language | English |
---|---|
Title of host publication | 2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 2930-2934 |
Number of pages | 5 |
ISBN (Electronic) | 9781728198354 |
DOIs | |
State | Published - 2023 |
Event | 30th IEEE International Conference on Image Processing, ICIP 2023 - Kuala Lumpur, Malaysia Duration: Oct 8 2023 → Oct 11 2023 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
---|---|
ISSN (Print) | 1522-4880 |
Conference
Conference | 30th IEEE International Conference on Image Processing, ICIP 2023 |
---|---|
Country/Territory | Malaysia |
City | Kuala Lumpur |
Period | 10/8/23 → 10/11/23 |
Funding
Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- de-hazing
- deep model
- generative adversarial networks
- learning
- physics-based