Denoising is a fundamental task in image processing, aimed at estimating an unknown
image from its noisy observation. In this paper, we develop a computationally simple …

Good learning image priors from the noise-corrupted images or clean natural images are
very important in preserving the local edge and texture regions while denoising images. This …

This paper proposes a detail-preserving image denoising method via cluster-wise
progressive principal component analysis (PCA) thresholding based on the Marchenko …

We propose a novel principal component analysis (PCA)-based image denoising framework
motivated by the fact that the PCA along with patch groups (PGs) can produce better …

Patch-based algorithms for image denoising have been widely used in recent years. Most of
patch-based methods just exploit patch redundancy in spatial or frequency domain without …

The goal of an image denoising algorithm is to preserve the details of clean images while
reducing the noise in noisy images. Some existing image denoising algorithms preserve the …

At present, most denoising algorithms cannot determine whether a pixel is noise, but these
use the same rules to process all pixels. Most denoising methods will filter out the original …

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 …

A novel image denoising algorithm using linear Bayesian maximum a posteriori (MAP)
estimation based on sparse representation model is proposed. Starting from constructing …

This paper presents an efficient image denoising scheme by using principal component
analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local …