由ag国际亚游主办的“信通论坛”本次邀请McMaster University的 Xiaolin Wu教授，与我校师生共同探讨A Fast and Practical CNN Method for Artful Image Regeneration。具体安排如下，欢迎感兴趣的师生参加。
一、主 题：A Fast and Practical CNN Method for Artful Image Regeneration
二、主讲人：Xiaolin Wu教授（IEEE Fellow,McMaster University）
Although artists’ actions in photo retouching, or artful image regeneration, appear to be highly nonlinear in nature and very difficult to model analytically, we find that the net effects from a mundane image to its final beautified version can be characterized, in most cases, by a parametric monotonically non-decreasing tone mapping function in the luminance axis and by a linear transform in the chrominance plane. This allows us to greatly simplify the existing CNN methods for mimicking the artists in photo retouching, and design a new holistic artful image regeneration network (HAIRNet). The objective of the HAIRNet is to learn the image-dependent parameters of the luminance tone mapping function and the linear chrominance transform, rather than learning the end-to-end pixel level mapping as in the standard practice of current CNN methods for image restoration and enhancement. The proposed new approach reduces the complexity of the neural network by two orders of magnitude, and as a beneficial side effect, it also improves the robustness and the generalization capability at the inference stage. The construction of the new AIRNet is made possible by an innovative technique of generating the required paired training images before and after photo retouchig.
Xiaolin Wu, Ph.D. in computer science, University of Calgary, Canada, 1988. Dr. Wu started his academic career in 1988, and has since been on the faculty of Western University, New York Polytechnic University (NYU Poly), and currently McMaster University. He holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing, computer vision multimedia signal coding and communication, joint source-channel coding, multiple description coding, and network-aware visual communication. He has published over two hundred-sixty research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow, McMaster Distinguished Engineering Professor, an associated editor of IEEE Transactions on Image Processing, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors.