Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Institute of Automation · Shandong Institute of Automation
Abstract
Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L_1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details.…
Citation impact
- FWCI
- 21.26
- Percentile
- 100%
- References
- 20
Authors
5- GMGaofeng MengCorresponding
Institute of Automation, Shandong Institute of Automation
- YWYing Wang
Institute of Automation, Shandong Institute of Automation
- JDJiangyong Duan
Shandong Institute of Automation, Institute of Automation
- SXShiming Xiang
Shandong Institute of Automation, Institute of Automation
- CPChunhong Pan
Institute of Automation, Shandong Institute of Automation
Topics & keywords
- Regularization (linguistics)
- Computer science
- Visibility
- Haze
- Constraint (computer-aided design)
- Image (mathematics)
- Computer vision
- Artificial intelligence
- Climate action