From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms
Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …
decades, the progress made in image denoising has benefited from the improved modeling …
Clustering-based denoising with locally learned dictionaries
P Chatterjee, P Milanfar - IEEE transactions on Image …, 2009 - ieeexplore.ieee.org
In this paper, we propose K-LLD: a patch-based, locally adaptive denoising method based
on clustering the given noisy image into regions of similar geometric structure. In order to …
on clustering the given noisy image into regions of similar geometric structure. In order to …
Learning how to combine internal and external denoising methods
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] …
to improve image denoising performance, as has been noted by several authors [11, 7] …
Adaptive image denoising by targeted databases
We propose a data-dependent denoising procedure to restore noisy images. Different from
existing denoising algorithms which search for patches from either the noisy image or a …
existing denoising algorithms which search for patches from either the noisy image or a …
Nonlocal hierarchical dictionary learning using wavelets for image denoising
R Yan, L Shao, Y Liu - IEEE transactions on image processing, 2013 - ieeexplore.ieee.org
Exploiting the sparsity within representation models for images is critical for image
denoising. The best currently available denoising methods take advantage of the sparsity …
denoising. The best currently available denoising methods take advantage of the sparsity …
A survey of edge-preserving image denoising methods
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 …
processing community. Often though, at the same time as reducing the noise in a signal, it is …
A weighted dictionary learning model for denoising images corrupted by mixed noise
This paper proposes a general weighted l 2-l 0 norms energy minimization model to remove
mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse …
mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse …
Image denoising via sequential ensemble learning
Image denoising is about removing measurement noise from input image for better signal-to-
noise ratio. In recent years, there has been great progress on the development of data …
noise ratio. In recent years, there has been great progress on the development of data …
Image denoising review: From classical to state-of-the-art approaches
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 …
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …
Image denoising via sparse and redundant representations over learned dictionaries
We address the image denoising problem, where zero-mean white and homogeneous
Gaussian additive noise is to be removed from a given image. The approach taken is based …
Gaussian additive noise is to be removed from a given image. The approach taken is based …