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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Improving Non-local Video Denoising with Local Binary Patterns and Image Quantization
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Improving Non-local Video Denoising with Local Binary Patterns and Image Quantization

机译：使用本地二进制模式和图像量化改进非本地视频降噪

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摘要

The most challenging aspect of video and image denoising is to preserve texture and small details, while filtering out noise. To tackle such problem, we present two novel variants of the 3D Non-Local Means (NLM3D) which are suitable for videos and 3D images. The first proposed algorithm computes texture patterns for each pixel by using the LBP-TOP descriptor to modify the NLM3D weighting function. It also uses MSB (Most Significant Bits) quantization to improve robustness to noise. The second proposed algorithm filters homogeneous and textured regions differently. It analyses the percentage of non-uniform LBP patterns of a region to determine whether or not the region exhibits textures and/or small details. Quantitative and qualitative experiments indicate that the proposed approaches outperform well known methods for the video denoising task, especially in the presence of textures and small details.
机译：视频和图像去噪的最具挑战性的方面是保留纹理和小的细节，同时滤除噪声。为了解决这一问题，我们提出了3D非局部均值（NLM3D）的两种新颖变体，它们适用于视频和3D图像。首先提出的算法通过使用LBP-TOP描述符修改NLM3D加权函数来计算每个像素的纹理图案。它还使用MSB（最高有效位）量化来提高对噪声的鲁棒性。第二种提出的算法对同质和纹理区域进行了不同的过滤。它分析区域的非均匀LBP模式的百分比，以确定该区域是否显示纹理和/或小细节。定量和定性实验表明，所提出的方法优于视频去噪任务的众所周知的方法，尤其是在存在纹理和小细节的情况下。

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