Connect Maximum A Posteriori (MAP) Inference with Convolutional Neural Network for Image RestorationMS52

The MAP model is a famous framework in the field of image restoration, and recent years have also witnessed the unprecedented success of CNNs in image denoising and super-resolution. In this talk, we intend to introduce some works on CNNs by referring to MAP inference in low level vision tasks: (1) design of a CNN denoiser, (2) extension of CNN denoisers for image restoration, and (3) other insights on CNN-based models delivered by MAP inference.

This presentation is part of Minisymposium “MS52 - A Denoiser Can Do Much More Than Just... Denoising (2 parts)
organized by: Yaniv Romano (Technion - Israel Institute of Technology) , Peyman Milanfar (Google Research) , Michael Elad (The Technion - Israel Institute of Technology) .

Wangmeng Zuo (Harbin Institute of Technology)
Lei Zhang (Hong Kong Polytechnic University)
Kai Zhang (Harbin Institute of Technology)
Shuhang Gu (The Hong Kong Polytechnic University)
bayesian methods, image reconstruction, inverse problems