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[学术报告] A New TV-Stokes Model for Image Deblurring and Denoising with Fast Algorithms
2017-05-04 16:59  
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报告题目:A New TV-Stokes Model for Image Deblurring and Denoising with Fast Algorithms

时间:20175610:00-11:00

地点:基础教学部C08201会议室

主讲人:贾志刚 教授 (江苏师范大学)

欢迎各位老师和同学参会!

报告摘要:

The famous TV-Stokes models, which improve the restored images comfortable, have been very successful in image denoising. In this talk, we propose a new TV-Stokes model for image deblurring with a good geometry explanation. In the tangential field smoothing, the data fidelity term is chosen to measure the distance between the solution and the orthogonal projection of the tangential field of the observation image onto the range of the conjugate of the blurry operator, while the total variation of the solution is chosen as the regularization term. In the image reconstruction, we compute the smoothing part of the image from the smoothed tangential field for the first step, and use an anisotropic TV model to obtain the texture part of the deblurred image. The solvability properties for the minimization problems in two steps are established, and fast algorithms are presented. Numerical experiments demonstrate that the new deblurring model can capture the details of images hidden in the blurry and noisy image, and the fast algorithms are efficient and robust.

报告人简历:

贾志刚 博士, 1980年出生,山东聊城人,江苏师范大学校聘教授、硕士生导师、第一批高层次人才队伍后备人选。主要研究方向为数值代数和图像处理,至今已发表学术论文20余篇,主持和参与国家自然科学基金项目3项和省部级科研项目3项。现兼职为江苏省计算数学学会常务理事,江苏省工业与应用数学会理事,美国Math Review评论员,SIMAX、JCAM等学术期刊的审稿人。       


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