A Review Paper : Noise Models in Digital Image Processing

Boyat, Ajay; Joshi, Brijendra Kumar

doi:10.5121/sipij.2015.6206

Cited by 278 publications

(134 citation statements)

References 15 publications

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“…The noise in an image may transpire from analog circuitry as electronic noise, photon noise, thermal agitation of ions, coding errors, etc, [11]. Blurring is a kind of noise that may occur because of improper camera setting or when the image has been captured from a moving platform [12].…”

Section: Introductionmentioning

confidence: 99%

“…The selection of a noise reduction algorithm depends greatly on the type of noise introduced in the image. The type of noise is either additive or multiplicative in nature [1,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Gaussian noise: The emergence of noise from amplifiers or sensors used during acquisition is electronic noise and is commonly known as Gaussian noise [1,11]. Gaussian noise basically disrupts the gray levels in an image and is uniformly distributed over the signal.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Denoising by Feature Recovery from Residual Noise in Spatial Domain

Girdher*,

Goyal,

Dogra

et al. 2019

IJRTE

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The growth of image processing techniques has lead to limitless applications of imaging in various fields of medical, remote sensing. However, the unpreventable problem of noise contamination arises during the processing of images. As the noise content in an image rises, it becomes difficult to denoise an image while preserving high-frequency edge features as well as low frequency smooth features. Minimal artefacts and better preservation of geometrical details such as edges and texture reflects efficient image reconstruction. Since many State-of-theart denoising algorithms have been reported in the literature, and there is always a compromise between noise removal and feature preservation. A novel approach for efficient noise removal along with recovery of fine features is being proposed. The idea behind denoising approach is the use of hybridization of spatial domain filters where base layer image and residual image are extracted and processed separately to mitigate the prevalence of artefacts and preserve image content. The performance of the proposed method is evaluated both quantitatively as well as qualitatively,and it is found that the proposed method could outperform existing denoising techniques.

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Section: Introductionmentioning

confidence: 99%

“…The selection of a noise reduction algorithm depends greatly on the type of noise introduced in the image. The type of noise is either additive or multiplicative in nature [1,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Denoising by Feature Recovery from Residual Noise in Spatial Domain

Girdher*,

Goyal,

Dogra

et al. 2019

IJRTE

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“…This noise may be Gaussian, salt and pepper and speckle [2]. Image de-nosing techniques can be classified in to two different categories special domain and transform domain.…”

Section: ( ) = ( ) + ( )mentioning

confidence: 99%

Hardware based Brain MR Image De-Noising using Weiner filter with Discrete Haar Wavelet Transform

Aruchamy

Bhattacharjee

Sanyal

2017

IJBSBT

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“…Speckle noise occurs when the back scattered echoes with spatial resolution less than that of the USI system interfere constructively or destructively, hence forming bright and dark spots which appear as granular structures on the reconstructed image [2]- [5]. Speckle patterns depict a form of multiplicative noise, and depends on the imaged tissue structures and various imaging parameters.…”

Section: Introductionmentioning

confidence: 99%