Fast Non-Local Algorithm for Image Denoising

Wang, Jin; Guo, Yanwen; Ying, Yiting; Liu, Yanli; Peng, Qunsheng

doi:10.1109/icip.2006.312698

Cited by 150 publications

(100 citation statements)

References 5 publications

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“…The objective of denoising is to remove the noise effectively while preserving the original image details as much as possible. So far, many approaches have been proposed to get rid of noise [12]. Traditionally, this is achieved by linear processing such as Wiener filtering [13,[14][15].…”

Section: Introductionmentioning

confidence: 99%

Image Restoration in Noisy Free Images Using Fuzzy Based Median Filtering and Adaptive Particle Swarm Optimization - Richardson-Lucy Algorithm

Kumar¹,

Shukla²,

Tripathi³

2017

IJIES

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Abstract:In this paper, we have proposed adaptive methods for image restoration in which the input images are affected by noise which is removed by fuzzy based median filter (FMF). The noise removed images from the FMF is appears to be so there is a need to restore the images with high quality. To restore the images an APSO (Adaptive particle swarm optimization) based Richardson-Lucy (R-L) algorithm is utilized. By both FMF and APSO-RL methods the denoising and restoration of the image is performed efficiently. The performance of the image denoising and restoration technique is evaluated by comparing the result of proposed technique with the existing denoising filter and GA, PSO methods. The comparison result shows a high-quality denoising and restoration ratio for the noisy images than the existing methods, in terms of peak signal-to-noise ratio (PSNR) and second-derivative-like measure of enhancement (SDME).

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Section: Introductionmentioning

confidence: 99%

Image Restoration in Noisy Free Images Using Fuzzy Based Median Filtering and Adaptive Particle Swarm Optimization - Richardson-Lucy Algorithm

Kumar¹,

Shukla²,

Tripathi³

2017

IJIES

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“…A strategy for reducing the complexity of patch distance computations while maintaining exact calculations is described in [25] and [7]. The method is known as integral images [24] (or summed area tables [6] in the context of texture mapping) and it allows to efficiently compute the sum of values of any image in a rectangular subset of a grid.…”

Section: Fast Nlm-p Using Integral Imagesmentioning

confidence: 99%

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Froment¹

2014

Image Processing on Line

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This article proposes a fast and open-source implementation of the well-known Non-Local Means (NLM) denoising algorithm, in its original pixelwise formulation. The fast implementation is based on the computation of patch distances using sums of lines that are invariant under a patch shift. The optimal parameters of NLM (in the average peak signal to noise ratio -PSNR -sense) are computed from an image database, thereby leading to a parameter-free NLM implementation. Comparison is performed with the parameter-free blockwise NLM implementation already proposed in IPOL journal by Buades, Coll and Morel. As expected the blockwise implementation offers better PSNR, at least when the noise standard deviation is large enough, but there is no significant difference in quality when performing visual inspection. The highlight is that the proposed parameter-free pixelwise NLM implementation is faster than the patchwise one by a factor of 6 to 49. Source CodeThe reviewed source code and documentation for the parameter-free fast pixelwise NLM algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

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“…Denoising methods on spatial domain cannot remove plenty of noise information but the image transform domain can often work wonders. Fourier Transforms in signal processing is one of the most important and widely used transform [15]. For two-dimensional discrete-time signal ( , ), = 0, 1, … , − 1, = 0, 1, … , − 1,, whose Fourier transform definitions are as follows:…”

Section: Fourier Transformmentioning

confidence: 99%

Smooth Optimization Technique for Denoising Image

Suguna¹,

Meena²

2018

IJET

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Noise in images can be characterized as a random variation of brightness information. It can be a by-product of image capture that conceals the desired information. All digital images are subject to different types of noise. Noise is the result of errors during acquisition process which affects the original pixel values in the image. Noise can be introduced in several ways depending on how the image is created. This paper suggests a mathematical model for representing noise in the image and solves the model for denoising. A comparison of smooth optimization algorithm with Fourier transform is presented with test image.

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Fast Non-Local Algorithm for Image Denoising

Cited by 150 publications

References 5 publications

Image Restoration in Noisy Free Images Using Fuzzy Based Median Filtering and Adaptive Particle Swarm Optimization - Richardson-Lucy Algorithm

Image Restoration in Noisy Free Images Using Fuzzy Based Median Filtering and Adaptive Particle Swarm Optimization - Richardson-Lucy Algorithm

Parameter-Free Fast Pixelwise Non-Local Means Denoising

Smooth Optimization Technique for Denoising Image

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