Simultaneous Video Defogging and Stereo Reconstruction
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence. In our formulation, the depth cues from stereo matching and fog information reinforce each other, and produce superior results than conventional stereo or defogging algorithms. We first improve the photo-consistency term to explicitly model the appearance change due to the scattering effects. The prior matting Laplacian constraint on fog transparency imposes a novel smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transparency at neighboring points. These novel constraints are formulated together in an MRF framework, which is optimized iteratively by introducing auxiliary variables. The experiment results on real videos demonstrate the strength of our method.
"Simultaneous Video Defogging and Stereo Reconstruction" Zhuwen Li, Ping Tan, Robby T. Tan, Danping Zou, Steven Zhiying Zhou and Loong-Fah Cheong. IEEE Conference on Computer Vision and Patten Recognition (CVPR), Boston, USA, Jun. 2015