报告人：郭志昌 哈尔滨工业大学副教授

报告时间：9月24日14:00

报告地点：腾 讯 ID：761 524 382

主办单位：数学与统计学院

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报告摘要：

    The BM3D method achieves excellent denoising performance, but it has artificial effects and bias effects and its performance largely depends on the noise level parameter. To address this, we propose a hybrid BM3D and PDE method for non-parametric single image denoising.  First, a non-local Perona-Malik (NLPM) filtering is proposed, and we prove its discontinuity maintaining, mean invariance, convergence, and local continuity. Based on these mathematical properties, an NLPM based noise level estimator (NLPM-NLE) is explored, which involves three steps: preprocessing by NLPM filtering, sample area selection, parameter estimation. And then, we advance a stable-BM3D (SBM3D) method with NLPM filtering to avoid artificial effects and bias effects. Finally, connecting the NLPM-NLE and SBM3D by merging the same part, we develop a non-parametric single image denoising (NPSID) method.  Additionally, our proposed BM3D method with NLPM-NLE and the NPSID are compared with other blind denoising methods including PCA+BM3D, WTP+BM3D, and ESM+BM3D on real image denoising. Experiments show that the proposed non-parametric method can automatically and effectively remove noise and preserve details.

郭志昌，哈尔滨工业大学数学学院副教授，博士生导师, 计算数学系副主任和计算数学研究所副所长，中国生物医学工程学会医学人工智能分会青年委员，主要分数阶方程的数值理论和在图像恢复中的建模，深度学习卷积神经网络的部分解释，基于PDE和深度学习卷积神经网络的融合模型等方面的研究。在SIAM系列和IEEE系列等高水平期刊上发表学术论文20余篇。现主持国家自然科学基金面上项目1项，曾主持结项国家和省部级项目4项，参与面上基金2项。