Literature Survey on Impulse Noise Reduction

Koli, Manohar; Balaji, Sekaran

doi:10.5121/sipij.2013.4506

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…4 shows the PDF of Salt and Pepper noise, if mean is zero and variance is 0.05. Here we will meet two spike one is for bright region (where gray level is less) called 'region a' and another one is dark region (where gray level is large) called 'region b', we have clearly seen here the PDF values are minimum and maximum in 'region a' and 'region b', respectively [16]. Salt and Pepper noise generally corrupted the digital image by malfunctioning of pixel elements in camera sensors, faluty memory space in storage, errors in digitization process and many more.…”

Section: Impulse Valued Noise (Salt and Pepper Noise)mentioning

confidence: 85%

A Review Paper : Noise Models in Digital Image Processing

Boyat¹,

Joshi²

2015

SIPIJ

278

115

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Section: Impulse Valued Noise (Salt and Pepper Noise)mentioning

confidence: 85%

A Review Paper : Noise Models in Digital Image Processing

Boyat¹,

Joshi²

2015

SIPIJ

278

115

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“…Noise is known to be an unwanted signal that can easily destroy the image quality [4]. This unwanted signal in an image can appear in the form of dsitored pixel values, which can show up images as uneven lines and blurry objects and can also be seen in film grain and in the shot noise of a photon detector [5] [6] [7].…”

Section: Adding Image Noisementioning

confidence: 99%

Anti-Interference Study on Radiographic Bone Age Estimation Based on Artificial Intelligence Model

Huang,

Chen

2023

OJRad

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In this paper, the interferences of X-ray image noise on a bone age model, Xception model, were studied. We conduct a comparative experiment test according to the output performance of the neural network model using both the original image training and noise-added (Gaussian noise plus salt-pepper noise) training, and analyze the anti-interference ability of the Xception model, hoping to improve it through noise enhancement training and generalize the application ability of the model. The results show that the model trained with noise-added (Gaussian noise plussalt-pepper noise) images can make predictions that are more robust and less affected by the image disturbances, such as image noise.

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“…Impulse noise can be further classified into salt-and-pepper noise and random valued noise. A saltand-pepper noise matches an extreme dynamic range of a pixel value [2]. In this case, noisy pixels can be quite easily detected by an Adaptive Median Filter (AMF).…”

Section: Introductionmentioning

confidence: 99%

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

Victoria.L¹,

Sathappan

2018

IJET

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Noise removal from the color images is the most significant and challenging task in image processing. Among different conventional filter methods, a robust Annihilating filter-based Low-rank Hankel matrix (r-ALOHA) approach was proposed as an impulse noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix to decompose the sparse impulse noise components from an original image. However, in this algorithm, the patch image was considered as it was sparse in the Fourier domain only. It requires an analysis of noise removal performance by considering the other transform domains. Hence in this article, the r-ALOHA can be extended into other transform domains such as log and exponential. In the log and exponential domain, the logarithmic and exponential functions are used for modeling the multiplicative noise model. But, this model is used only for positive outcomes. Therefore, wavelet transform domain is applied to the noise model that localizes an image pixel in both frequency and time domain simultaneously. Moreover, it separates the most vital information in a given image. Thus, it is feasible for obtaining a better approximation of the considered function using few coefficients. Finally, the experimental results show the performance effectiveness of the proposed algorithm.

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Literature Survey on Impulse Noise Reduction

Abstract: In every image processing algorithm quality of image plays a very vital

Cited by 10 publications

References 8 publications

A Review Paper : Noise Models in Digital Image Processing

A Review Paper : Noise Models in Digital Image Processing

Anti-Interference Study on Radiographic Bone Age Estimation Based on Artificial Intelligence Model

A sporadic decomposition of Hankel structured matrix in logarithmic and wavelet domain for impulse noise removal

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