預告:龐志峰:Image restoration via the local adaptive TV-based regularization

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2019-05-17 來源:數學與統計學院

報告承辦單位:數學與統計學院

報告內容: Image restoration via the local adaptive TV-based regularization

報告人姓名: 龐志峰

報告人所在單位: 河南大學數學與統計學院

報告人職稱/職務及學術頭銜:副教授/碩導

報告時間: 2019年5月20日上午10:00—11:00

報告地點: 理科樓A419

報告人簡介: 龐志峰博士,河南大學數學與統計學院副教授, 碩士生導師。目前任河南省數字圖形圖像學會常務理事和秘書長, 并分別兼任該學會的智能精準放療專業委員會和智能信息融合專業委員會副主委, 同時任中國生物醫學工程學會醫學人工智能專委會青年委員會委員和中國工業與應用數學學會數學與醫學交叉委員會委員。主要研究圖像處理中的數學理論與數值算法。曾主持國家自然科學基金1項, 參與國家自然科學基金2項, 國家重點基礎研究發展計劃(973項目)1項。現發表相關學術論文27篇(其中SCI收錄25篇), 授權專利1項。

報告摘要:Image denoising problem still remains an active research field in the image processing. In the proposed model, how to describe the local structure of image is very important to improve the denoising quality. This paper proposes an image denoising model based on the adaptive weighted TVp regularization, where the regularization term can efficiently depict local structures by coupling the rotation matrix and the weighted matrix into the TVp-quasinorm. The adaptive angle used in the rotation matrix via the orientation field estimation mainly depends on the average phase angle of pixels within a suitable window, so this approach is more reasonable to express the local structure information. In addition, since the proposed model is nonsmooth and non-Lipschitz, we employ the alternating direction method of multipliers (ADMM) to solve it based on the half-quadratic scheme for solving the related ?2 ? ?p subproblem. We prove the convergence of the half-quadratic scheme under the framework of the alternating direction method (ADM) with a gradually decreasing smooth parameter. Furthermore, we also discuss the convergence of the ADMM. Some numerical comparisons with the classic TV-based models illustrate the good performance of our proposed model for the image denoising problem.

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