Upcoming Webinar: "Toward Efficient and Flexible CNN-based Denoising in Photography" presented by Dr. Wangmeng Zuo

Upcoming Webinar! December 10, 2019
Webinar Topic: "Toward Efficient and Flexible CNN-based Denoising in Photography" 

Presented by Dr. Wangmeng Zuo and based on an IEEE Xplore article
published in IEEE Transactions on Image Processing

About this Topic:

This webinar, “Toward Efficient and Flexible CNN-based Denoising in Photography,” begins with the design of denoising CNN (DnCNN) model by incorporating residual learning and batch normalization. Then, a flexible denoising CNN (FFDNet) is presented to handle Gaussian denoising with any or even spatially variant noise levels. Finally, several remarks are provided to develop CNNs for denoising in real photography by incorporating noise level estimation and non-blind denoising.

About the Presenter: 

Dr. Wangmeng Zuo received his Ph.D. degree in computer application technology from the Harbin Institute of Technology, China, in 2007. From 2004 to 2006, he was a Research Assistant with the Department of Computing, The Hong Kong Polytechnic University. From 2009 to 2010, he was a Visiting Professor with Microsoft Research Asia. He is currently a Professor with the School of Computer Science and Technology, at the Harbin Institute of Technology. He has published over 90 papers in top-tier academic journals and conferences. His current research interests include image enhancement and restoration, image generation and editing, visual tracking, object detection, and image classification. He has served as a Tutorial Organizer in ECCV 2016, an Associate Editor of the IET Biometrics, and the Guest Editor of Neurocomputing, Pattern Recognition, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Transactions on Neural Networks and Learning Systems. He has been a Senior Member of the IEEE since 2014.