Statistical interpretation of non-local means

A Statistical Interpretation of Non-Local Means

Noise filtering is a common step in image processing, and is particularly effective in improving the subjective quality of images. A large number of techniques have been developed, many of which concentrate on the problem of removing noise without damaging small structures such as edges. One recent approach that demonstrates empirical merit is the non-local means (NLM) algorithm. However, in or...

Non-Local Means Denoising

We present in this paper a new denoising method called non-local means. The method is based on a simple principle: replacing the color of a pixel with an average of the colors of similar pixels. But the most similar pixels to a given pixel have no reason to be close at all. It is therefore licit to scan a vast portion of the image in search of all the pixels that really resemble the pixel one w...

Pruned non-local means

In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches. We demonstrate that the denoising performance of NLM can be improved by pruning the neighboring pixels, namely, by rejecting neighboring pixels whose weights are below a certain threshold λ. While pruning can potentially reduce pixel ...

[Proceeding] An explicit mechanical interpretation of Eringen non-local elasticity by means of fractional calculus

Comparative Study of Non-local Means and Fast Non –local Means Algorithm for Image Denoising

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age. All digital images contain some degree of noise. Removing noise from the original signal is still a challenging problem for researchers. In this paper, the non-local denoising approach presented by Buades et al. is compared and analyzed by Fast nonlocal means algorithm. Th...