Conference proceeding
Factorizing Scene Albedo and Depth from a Single Foggy Image
2009 IEEE 12TH INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), pp 1701-1708
01 Jan 2009
Abstract
Atmospheric conditions induced by suspended particles, such as fog and haze, severely degrade image quality. Restoring the true scene colors (clear day image) from a single image of a weather-degraded scene remains a challenging task due to the inherent ambiguity between scene albedo and depth. In this paper, we introduce a novel probabilistic method that fully leverages natural statistics of both the albedo and depth of the scene to resolve this ambiguity. Our key idea is to model the image with a factorial Markov random field in which the scene albedo and depth are two statistically independent latent layers. We show that we may exploit natural image and depth statistics as priors on these hidden layers and factorize a single foggy image via a canonical Expectation Maximization algorithm with alternating minimization. Experimental results show that the proposed method achieves more accurate restoration compared to state-of-the-art methods that focus on only recovering scene albedo or depth individually.
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Details
- Title
- Factorizing Scene Albedo and Depth from a Single Foggy Image
- Creators
- Louis Kratz - Drexel UniversityKo Nishino - Drexel University
- Publication Details
- 2009 IEEE 12TH INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), pp 1701-1708
- Series
- IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000294955300219
- Scopus ID
- 2-s2.0-77953179567
- Other Identifier
- 991019168676704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic