应002资讯网邀请，英国利物浦大学陈柯教授将于近日来访公司，并做图像处理模型与算法分析系列报告，以下是报告信息，欢迎感兴趣的师生参加。

   报告1时间:2020年1月6日（星期一）下午14:00-15:30

   地点：格物楼503

   报告题目：On Optimization Models and Methods for Inverse Problems from Imaging

   报告简介：

 Optimization is often viewed as an active and yet mature research field. However the recent and rapid development in the emerging field of Imaging Sciences has provided a very rich source of new problems as well as big challenges for optimization. Such problems having typically non-smooth and non-convex functionals demand urgent and major improvements on traditional solution methods suitable for convex and differentiable functionals.

This talk presents a limited review of a set of Imaging Models which are investigated by the Liverpool group as well as other groups, out of the huge literature of related works. We start with image restoration models regularised by the total variation and high order regularizers. We then show some results from image registration to align a pair of images which may be in single-modality or multimodality with the latter very much non-trivial. Next we review the variational models for image segmentation. Finally we show  some recent attempts to extend our image registration models from more traditional optimization to the

Deep Learning framework.

Joint work with recent and current collaborators including D P Zhang, A Theljani, M Roberts, J P Zhang, A Jumaat, T Thompson.

   报告2时间：2020年1月8日（星期三）下午14:00-15:30

   报告地点：格物楼503

   报告题目：On A New Image Algorithm for Simultaneously Bias Correction and Co-Registration：Part I

   报告3时间：2020年1月8日（星期三）下午15:45-17:15

   地点：格物楼503

   报告题目：On A New Image Algorithm for Simultaneously Bias Correction and Co-Registration：Part II

   报告简介：

Image Registration is a widely studied, yet still challenging and active, task in image processing, especially in medical imaging, because it is capable of tracking changes between treatments or complementing information between modalities.

This talk will first give a brief review the state of art in these research directions and then focus on how to design models to register images one of which has a bias field or artefacts. This acute setting is neither a mono-modal nor a multi-modal image registration problem. The presentation gives a new joint formulation that improves the traditional framework of removing bias first and then registering. Moreover, we overcome the problem of having to choose several parameters (a typical challenge when dealing with joint models) by reformulation via game theory. A key result is to show that the new game framework has a Nash equilibrium when the Nash theory is not applicable. To solve the model numerically, we use an alternating minimization algorithm in the discrete setting.  Finally, numerical results can show that the new model outperforms existing models.

The talk also contains joint work with D P Zhang and Anis Theljani.

   报告人简介：陈柯，英国利物浦大学终身教授，英国IMA Fellow、御批数学家。陈柯教授作为CMIT和LCMH创新团队负责人，主要研究方向为计算数学、应用数学、图像处理、医疗应用等，在SIA M Journal on Imaging Sciences、IEEE Transactions on Image Processing、Journal of Computational Physics等国际权威期刊上发表超过170余篇学术论文。目前担任Numerical Algorithms，Journal of Mathematical Research and Exposition，Journal of Applied Mathematics ，International Journal of Computer Mathematics，Journal of Mathematical Research with Applications等多个国际知名期刊的编辑及执行编辑。