Abstract
Although the nonlocal means (NLM) algorithm takes a significant step forward in image filtering field, it suffers from a high computational complexity. To deal with this drawback, this paper proposes an acceleration strategy based on a correlation operation. Instead of per-pixel processing, this approach performs a simultaneous calculation of all the image pixels with the help of correlation operators. Complexity analysis and experimental results are reported and show the advantage of the proposed algorithm in terms of computation and time cost.
Highlights
Digital image denoising has been a fundamental and challenging issue for several decades [1, 2]
In this paper, compared with the traditional nonlocal means (NLM) algorithm, in which the calculations of weights are by the pixel by pixel way, our proposed fast strategy was performed on the whole image
We proposed a correlation based strategy to accelerate NLM algorithms
Summary
Digital image denoising has been a fundamental and challenging issue for several decades [1, 2]. The algorithm of nonlocal means (NLM) filtering was proposed by Buades et al [8]. They suggested that a denoised pixel is equivalent to the weighted average of its neighboring pixels, with the weights calculated by the normalized Gaussian weighted Euclidean distance between the blocks centred at those pixels. In this paper, compared with the traditional NLM algorithm, in which the calculations of weights are by the pixel by pixel way, our proposed fast strategy was performed on the whole image.
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