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Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms

机译:通过可分离的4D非局部时空变换对视频进行去噪,去块和增强

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摘要

We propose a powerful video filtering algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher dimensional transform-domain representation of the observations is leveraged to enforce sparsity, and thus regularize the data: 3-D spatiotemporal volumes are constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are then grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e., self-similarity) along the fourth dimension of the group. Collaborative filtering is then realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, the collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original positions in the video. The proposed filtering procedure addresses several video processing applications, such as denoising, deblocking, and enhancement of both grayscale and color data. Experimental results prove the effectiveness of our method in terms of both subjective and objective visual quality, and show that it outperforms the state of the art in video denoising.
机译:我们提出了一种强大的视频过滤算法,该算法利用了表征自然视频序列的时间和空间冗余。该算法实现了非局部分组和协作过滤的范例,其中利用观测值的高维变换域表示来实施稀疏性,从而使数据规范化:3-D时空体积是通过沿着由定义的轨迹跟踪块来构造的运动矢量。然后,将相互相似的体积沿另一个第四维堆叠在一起,从而将它们分组在一起,从而生成一个称为4D结构的组,其中沿不同维度存在不同类型的数据相关性:沿着块的两个维度的局部相关性,时间上的沿运动轨迹的相关性和沿组第四维的非局部空间相关性(即自相似性)。然后,通过去相关的4-D可分离变换对每个组进行变换,然后通过收缩和逆变换来实现协作过滤。以这种方式,协作过滤为组中堆叠的每个体积提供了估计值,然后将这些估计值返回并自适应地聚合到它们在视频中的原始位置。所提出的过滤过程解决了几种视频处理应用,例如去噪,去块以及灰度和彩色数据的增强。实验结果证明了我们的方法在主观和客观视觉质量方面都是有效的,并表明它在视频去噪方面优于最新技术。

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