Department of Electrical Engineering, University of California, Santa Cruz, CA 95064, USA.
IEEE Trans Image Process. 2012 Apr;21(4):1635-49. doi: 10.1109/TIP.2011.2172799. Epub 2011 Oct 19.
In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.
在本文中，我们提出了一种去噪方法，该方法受到我们之前对图像去噪性能边界分析的启发。本研究中的见解被用于推导出一种高性能的实用去噪算法。我们提出了一种基于补丁的维纳滤波器，该滤波器利用补丁冗余来进行图像去噪。我们的框架使用几何和光度相似的补丁来估计不同的滤波器参数。我们描述了如何直接从输入的噪声图像准确地估计这些参数。我们的去噪方法旨在实现接近最优的性能（均方误差意义上），具有详细分析的合理统计基础。我们的方法在各种图像和噪声水平上的实验验证结果表明，我们提出的方法在视觉和定量方面均与当前的最先进方法相当或超过。
