Impulsive noise in images: a brief review

Maity, Alenrex; Chatterjee, Rishav

doi:10.19101/tipcv.2017.39025

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The optimal filter for each type of noise was selected based on a comprehensive analysis of the literature, that is, BM3D [11] for G n , median [12] for S n , TV filters were selected for P n [13][14][15]. The application of these filters to an image is shown in the following program code:…”

Section: Methodsmentioning

confidence: 99%

Algorithm for improving the quality of mixed noisy images

Mamatov,

Jalelova,

Samijonov

et al. 2024

J. Phys.: Conf. Ser.

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Digital images are one of the effective means of receiving, transmitting, exchanging and expressing information. However, the digital image is not always of the expected quality, that is, as a result of the influence of various factors, noises appear in the image. This significantly reduces the accuracy and quality of the data in the image. Segmentation and recognition of objects from a low-quality image creates many additional problems. For example, it requires image quality enhancement based on powerful pre-processing algorithms. One such problem is to eliminate multiple noises in the image, and it is one of the pressing issues of digital image processing. This paper is devoted to solving the problem of image reconstruction by eliminating the mixed condition of Gaussian, salt-pepper and Poisson noises, which are common in digital images, in which one filter that optimally reduces each type of noise is selected and all combinations of them are applied to the mixed noise image to maximize the images. The idea of restoration has been put forward. The quality of the reconstructed images was evaluated by the NIQE criterion, which is one of the no-reference quality indicators, and a rule for automating the image processing process was proposed based on its values.

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Section: Methodsmentioning

confidence: 99%

Algorithm for improving the quality of mixed noisy images

Mamatov,

Jalelova,

Samijonov

et al. 2024

J. Phys.: Conf. Ser.

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“…From the research [25], it can be inferred that noise also affects keypoints detection, because noise is formed through random variation of intensity of pixels in an image. This also means that when there is noise, there may be changes in the value of pixel which can affect the detection result of feature detector.…”

Section: ) Minimum Eigenvaluementioning

confidence: 99%

Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points

Kusnadi

Ngadiman

Pane

et al. 2020

EECSI

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This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing lowfrequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK.

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“…For image or signal processing, a well known unwanted noise is the impulse noise 1 (the salt and pepper in an image). This noise is modelized through fixed values or in a more general framework by some random values [24].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter With Impulse Noised Outliers : a Robustsequential Algorithm to Filter Data With a Large Number Of outliers

Cloez

Fontez

Gonzalez-Garcia

et al. 2022

Preprint

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Impulse noised outliers are data points that differs significantly from other observations. They are generally removed from the data set through local regression or Kalman filter algorithm. However, these methods, or their generalizations, are not well suited when the number of outliers is of the same order as the number of low-noise data. In this article, we propose a new model for impulse noised outliers based on simple latent linear Gaussian processes as in the Kalman Filter. We present a fast forward-backward algorithm to filter and smooth sequential data and which also detect these outliers. We compare the robustness and efficiency of this algorithm with classical methods. Finally, we apply this method on a real data set from a Walk Over Weighing system admitting around $60\%$ of outliers. For this application, we further develop an (explicit) EM algorithm to calibrate some algorithm parameters.

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Impulsive noise in images: a brief review

Cited by 11 publications

References 16 publications

Algorithm for improving the quality of mixed noisy images

Algorithm for improving the quality of mixed noisy images

Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points

Kalman Filter With Impulse Noised Outliers : a Robustsequential Algorithm to Filter Data With a Large Number Of outliers

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