视频去噪中nl均值的自适应正则化

Camille Sutour, Jean-François Aujol, C. Deledalle, J. Domenger
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引用次数: 3

摘要

我们提出了一种基于非局部均值自适应正则化的去噪方法。NL-means利用自然图像中的冗余来降低噪声。他们计算出周围距离较近的像素的加权平均值。该方法性能良好,但在奇异结构上存在残余噪声。我们使用在nl均值中计算的权重作为去噪过程性能的度量。这些权重平衡了自适应ROF模型中的数据保真度项,从而在局部执行自适应电视正则化。此外,该模型可以适应不同的噪声统计量,并且在指数族的一般情况下可以计算出快速的分辨率。我们通过使用时空补丁将该模型应用于视频去噪。与空间补丁相比,它们提供了更好的时间稳定性,而自适应电视正则化校正了在移动结构周围观察到的残余噪声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive regularization of the NL-means for video denoising
We derive a denoising method based on an adaptive regularization of the non-local means. The NL-means reduce noise by using the redundancy in natural images. They compute a weighted average of pixels whose surroundings are close. This method performs well but it suffers from residual noise on singular structures. We use the weights computed in the NL-means as a measure of performance of the denoising process. These weights balance the data-fidelity term in an adapted ROF model, in order to locally perform adaptive TV regularization. Besides, this model can be adapted to different noise statistics and a fast resolution can be computed in the general case of the exponential family. We adapt this model to video denoising by using spatio-temporal patches. Compared to spatial patches, they offer better temporal stability, while the adaptive TV regularization corrects the residual noise observed around moving structures.
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