The nonlocal means (nlm) filter is one of the most popular denoising approaches and there have been many improvements regarding its weight function and parameter optimisation. However, those improvements have not remo...
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The nonlocal means (nlm) filter is one of the most popular denoising approaches and there have been many improvements regarding its weight function and parameter optimisation. However, those improvements have not removed artifacts such as the false texture pattern, which occurs when the smoothing parameter in the weight function is small. The smoothness in the flat region and the sharpness in the texture region without the artifacts can be ensured by accurately estimated weights, which can be calculated with sufficient similar patches. In this reported work, a nlm algorithm with weight update is used to exploit the weights from relatively similar locations as well as the weights from the centre location. Experimental results demonstrate that the proposed method performed well.
The non-local means (nlm) algorithm suppresses noise via replacing the noisy pixel by the weighted average of all the pixels with similar neighbourhood vectors. However, the weights calculation is computationally expe...
详细信息
The non-local means (nlm) algorithm suppresses noise via replacing the noisy pixel by the weighted average of all the pixels with similar neighbourhood vectors. However, the weights calculation is computationally expensive, as a result of which the nlm algorithm is quite slow for practical applications. A random projection approach is introduced to reduce the dimension of neighbourhood vectors used in the nlm filtering process, which yields a faster and more accurate denoising effect. Experimental results illustrate the effectiveness of the proposed method.
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