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 …

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 …

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 …

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 …

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 …

Image denoising algorithms often assume an additive white Gaussian noise (AWGN)
process that is independent of the actual RGB values. Such approaches are not fully …

Noise is always presents in digital images during image acquisition, coding, transmission,
and processing steps. Noise is very difficult to remove it from the digital images without the …

Different methods for image denoising have complementary strengths and can be combined
to improve image denoising performance, as has been noted by several authors [11, 7] …

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 …