報(bào)告承辦單位:數(shù)學(xué)與統(tǒng)計(jì)學(xué)院

報(bào)告內(nèi)容: Image restoration via the local adaptive TV-based regularization

報(bào)告人姓名: 龐志峰

報(bào)告人所在單位: 河南大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院

報(bào)告人職稱(chēng)/職務(wù)及學(xué)術(shù)頭銜:副教授/碩導(dǎo)

報(bào)告時(shí)間: 2019年5月20日上午10:00—11:00

報(bào)告地點(diǎn): 理科樓A419

報(bào)告人簡(jiǎn)介: 龐志峰博士，河南大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院副教授, 碩士生導(dǎo)師。目前任河南省數(shù)字圖形圖像學(xué)會(huì)常務(wù)理事和秘書(shū)長(zhǎng), 并分別兼任該學(xué)會(huì)的智能精準(zhǔn)放療專(zhuān)業(yè)委員會(huì)和智能信息融合專(zhuān)業(yè)委員會(huì)副主委, 同時(shí)任中國(guó)生物醫(yī)學(xué)工程學(xué)會(huì)醫(yī)學(xué)人工智能專(zhuān)委會(huì)青年委員會(huì)委員和中國(guó)工業(yè)與應(yīng)用數(shù)學(xué)學(xué)會(huì)數(shù)學(xué)與醫(yī)學(xué)交叉委員會(huì)委員。主要研究圖像處理中的數(shù)學(xué)理論與數(shù)值算法。曾主持國(guó)家自然科學(xué)基金1項(xiàng), 參與國(guó)家自然科學(xué)基金2項(xiàng), 國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973項(xiàng)目)1項(xiàng)?，F(xiàn)發(fā)表相關(guān)學(xué)術(shù)論文27篇(其中SCI收錄25篇), 授權(quán)專(zhuān)利1項(xiàng)。

報(bào)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.