Abstract:
At the crossing of the statistical and functional analysis, there exists a relentless quest for an efficient image
denoising algorithm. In terms of greyscale imaging, a plethora of denoising algorithms have been documented in
the literature, in spite of which the level of functionality of these algorithms still holds margin to acquire desired
level of applicability. Quite often noise affecting the pixels in image is Gaussian in nature and uniformly deters
information pixels in image. Based on some specific set of assumptions all methods work optimally, however
they tend to create artefacts and remove fine structural details under general conditions. This article focuses on
classifying and comparing some of the significant works in the field of denoising.
