Domain decomposition method for image denosing and image deblurring

Start: 10/16/2013 - 1:15pm
End  : 10/16/2013 - 2:15pm

Applied Math Seminar

Xu Jing (Zhejiang Gongshang University, Currently she is visiting UCLA)



Image restoration has drawn much attention in recent years and a surge of research has been done on variational models and their numerical studies. However, there remains an urgent need to develop fast and robust methods for solving the minimization problems and the underlying nonlinear PDEs to process image of moderate to large size. We proposed a two –level domain decomposition method, which consists of an overlapping domain decomposition technique and a coarse mesh correction, for directly solving the total variational minimization problems. What’s more, the domain decomposition method hadn't been applied directly to image deblurring because of the global character of blur operator. In order to avoid separating the blur operator, we propose an algorithm for directly solving the total variational minimization problems with domain decomposition method. Various numerical experiments and comparisons demonstrate that the proposed method is efficient and fast particularly for images of large size.


Dr. Jing Xu is an associate professor in Zhejiang Gongshang University, Hangzhou, China. Now she is a visiting scholar of Professor Luminita Vese in mathematics in UCLA from July 1, 2013 to June 30, 2014. Her research major is the applications of partial differential equations to image processing. She got her PHD degree in applied mathematics institute of Chinese Academy Science, 2007. In that time, she studied the multigrid method in image restoration from Professor Qianshun Chang. Jing Xu has one year experience being a research fellow in Nanyang Technology University, Singapore from 2008 to 2009. During that time, she studied domain decomposition method from Professor Xuecheng Tai and lilian Wang.

CMC Campus, Adams Hall, Davidson (the largest lecture room on the first floor)