报告题目：Simultaneous Video Defogging and Stereo Reconstruction
报告人：Ping Tan，博士，加拿大西蒙弗雷泽大学（Simon Fraser University, Canada）
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 transmission imposes a detail-preserving smoothness constraint on the scene depth. We further enforce the ordering consistency between scene depth and fog transmission 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.
Dr. Ping Tan obtained his PhD degree from the Hong Kong University of Science and Technology in 2007. After that, he joined the National University of Singapore as an assistant professor and was promoted to associate professor in 2014. Dr. Tan is now an assistant professor in the Simon Fraser University. Dr. Tan’s research interests include computer vision, graphics, and robotics. He received the inaugural TR35@Singapore award, and the IVC outstanding young researcher honorable mention award, both in 2012. Dr. Tan serves in the editorial board of the International Journal of Computer Vision (IJCV), Computer Graphics Forum (CGF), Machine Vision and Applications (MVA), and Unmanned Systems.