A physical approach to color image understanding
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Factorial Markov Random Fields
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
All the Images of an Outdoor Scene
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2008 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deep photo: model-based photograph enhancement and viewing
ACM SIGGRAPH Asia 2008 papers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Contrast restoration of weather degraded images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Stereo reconstruction and contrast restoration in daytime fog
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Atmospheric conditions induced by suspended particles, such as fog and haze, severely alter the scene appearance. Restoring the true scene appearance from a single observation made in such bad weather conditions remains a challenging task due to the inherent ambiguity that arises in the image formation process. In this paper, we introduce a novel Bayesian probabilistic method that jointly estimates the scene albedo and depth from a single foggy image by fully leveraging their latent statistical structures. 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 and to jointly estimate them. We show that we may exploit natural image and depth statistics as priors on these hidden layers and estimate the scene albedo and depth with a canonical expectation maximization algorithm with alternating minimization. We experimentally evaluate the effectiveness of our method on a number of synthetic and real foggy images. The results demonstrate that the method achieves accurate factorization even on challenging scenes for past methods that only constrain and estimate one of the latent variables.