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 …

Removing noise from the original signal is still a challenging problem for researchers. There
have been several published algorithms and each approach has its assumptions …

Reducing noise has always been one of the standard problems of the image analysis and
processing community. Often though, at the same time as reducing the noise in a signal, it is …

With the explosion in the number of digital images taken every day, the demand for more
accurate and visually pleasing images is increasing. However, the images captured by …

Image denoising has been a well studied problem in the field of image processing. Yet
researchers continue to focus attention on it to better the current state-of-the-art. Recently …

The search for efficient image denoising methods still is a valid challenge, at the crossing of
functional analysis and statistics. In spite of the sophistication of the recently proposed …

Image denoising continues to be an active research topic. Although state-of-the-art
denoising methods are numerically impressive and approch theoretical limits, they suffer …

Most existing state-of-the-art image denoising algorithms are based on exploiting similarity
between a relatively modest number of patches. These patch-based methods are strictly …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

The goal of natural image denoising is to estimate a clean version of a given noisy image,
utilizing prior knowledge on the statistics of natural images. The problem has been studied …