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Fast non-local means with size-adaptive search window

机译:快速的非本地方法,具有自适应大小的搜索窗口

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In this study, the authors present a fast non-local means (NLM) image denoising algorithm with size-adaptive search window. On the basis of the edge gradient and direction of the noisy image, the proposed fast scheme divides all the pixels into significant edge, moderate edge or non-edge region, and the size of the search window for most pixels which belong to non-edge region can be reduced. The proposed fast scheme also adopts different strategies to pre-select similar patches in the search window for efficient NLM denoising. Experimental results show that compared with the standard NLM method, the proposed fast scheme achieves a substantial reduction in computational cost and improvement in the denoising performance, both in terms of visual quality and numerical results.
机译:在这项研究中,作者提出了一种具有尺寸自适应搜索窗口的快速非局部均值(NLM)图像去噪算法。基于噪声图像的边缘梯度和方向,所提出的快速方案将所有像素划分为有效边缘,中等边缘或非边缘区域,以及大多数属于非边缘像素的搜索窗口的大小区域可以减少。所提出的快速方案还采用不同的策略来在搜索窗口中预先选择相似的补丁,以进行有效的NLM去噪。实验结果表明,与标准的NLM方法相比,所提出的快速方案在视觉质量和数值结果方面都显着降低了计算成本,并提高了降噪性能。

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