Nonlocal self-similarity of images has attracted considerable interest in the field of image
processing and has led to several state-of-the-art image denoising algorithms, such as block …

Image denoising problem still remains an active research field in the image processing. To
improve the denoising quality, it is very important to describe the local structure of the image …

Natural image modeling plays a key role in many vision problems such as image denoising.
Image priors are widely used to regularize the denoising process, which is an illposed …

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 …

This paper proposes a new image denoising approach using adaptive signal modeling and
adaptive soft-thresholding. It improves the image quality by regularizing all the patches in …

In this paper, we propose a very simple and elegant patch-based, machine learning
technique for image denoising using the higher order singular value decomposition …

This work considers noise removal from images, focusing on the well-known K-SVD
denoising algorithm. This sparsity-based method was proposed in 2006, and for a short …

In this paper we propose a generic recursive algorithm for improving image denoising
methods. Given the initial denoised image, we suggest repeating the following “SOS” …

We present a novel approach to still image denoising based on effective filtering in 3D
transform domain by combining sliding-window transform processing with block-matching …

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] …