Abstract：The nonlocal means (NLM) is a spatial domain image denoising method, and it exploits long range similarities between pixels of natural images. Notably, the similarity between true pixel values in original NLM is estimated based on patch information of noise-corrupted input image. In this paper, the pixel similarities in NLM are estimated based on Log-Gabor features to achieve good denoising results. Moreover, the mixed similarity combining the Log-Gabor features with intensity information is exploited to get better adaptivity to local image characteristics andfurther improve the denoising quality. In addition, the random projection-based NLM speed-up method is studied based on Johnson-Lindenstrauss lemma. Extensive tests including the running time comparison before and after dimensionality reduction, the impact of types of projection matrices and the extent of dimensionality reduction on final denoising performances are carried out. The experimental results confirm the effectiveness of the proposed acceleration scheme.
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2015, Vol. 28 
