An effective hybrid filter for the removal of Gaussian-impulsive noise in computed tomography images

Madhura, J; Babu, D. R. Ramesh

doi:10.1109/icacci.2017.8126108

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Several types of distortion might exist. Some of the common types that often exist are Gaussian, Impulse and Speckle Noise, where Gaussian Noise is additive, speckle is multiplicative and Impulse Noise is a combination of both [2] [3]. At the same time we observe that different kind of noise might exist in a single image, with varying intensities.…”

Section: Introductionmentioning

confidence: 82%

Efficient Removal of Noise from Image using Non-Linear Filtering

Ghosh¹,

Roy²,

Das³

2019

International Journal of Innovative Research in Electrical, Ele

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An image may contain different types of information related to parameters such as colour and brightness, the irregular variation of which leads to noise in an image. Images often get corrupted by different kind of noise such as Impulse, Gaussian and Salt-and-Pepper Noise, during image acquisition or transmission caused by the digital sensor attempting to record/capture tiny amount of light. Since an image appears in multiple resolutions in different forms, an image can be segmented into different classifying features. As a result, signal and noise can be separated and elimination of noise becomes easier. In this article, a non-linear median filter is preferred, which is found to be useful in reducing not only Impulse Noise but also Gaussian and Speckle Noise. The corresponding analyses are performed on grayscale images, where the results obtained through non-linear filtering are superior to results obtained using Average, Gaussian and Weiner filters.

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Section: Introductionmentioning

confidence: 82%

Efficient Removal of Noise from Image using Non-Linear Filtering

Ghosh¹,

Roy²,

Das³

2019

International Journal of Innovative Research in Electrical, Ele

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“…The results obtained in [24] show a better performance than the fuzzy vector median filter and the fuzzy vector directional filter in [25], the fuzzy ordered vector median filter and the fuzzy ordered vector directional filter in [26], the adaptive nearestneighbour filter in [27], the adaptive nearest-neighbour multichannel filter in [28], the directional distance filter in [2λ], the alpha-trimmed vector median filter in [30], the hybrid directional filter (HDF), the entropy vector median filter and the adaptive HDF described in [31]. In the proposed work median-based filters of the following types are studiedμ Non-adaptive median-based filters: The proposed filter in [32] is a non-linear filter, and its implementation consists of the following stepsμ (i) segment the image into different regions, (ii) determine the Gaussian distribution, (iii) calculate the median value of the pixels, (iv) remove the corrupted pixels, (v) reconstruct the denoised image sections, and (vi) obtain the final denoised image. This proposed filter was successfully applied to computed tomography scans with low image quality.…”

Section: Non-linear Filtersmentioning

confidence: 99%

“…The PSNR measure is defined asμ where MSE is the mean square error as in (32) with max (x) defining the maximum pixel intensity of image I…”

Section: Iet Image Process © the Institution Of Engineering And Techmentioning

confidence: 99%

“…Non‐adaptive median‐based filters: The proposed filter in [32] is a non‐linear filter, and its implementation consists of the following steps: (i) segment the image into different regions, (ii) determine the Gaussian distribution, (iii) calculate the median value of the pixels, (iv) remove the corrupted pixels, (v) reconstruct the denoised image sections, and (vi) obtain the final denoised image. This proposed filter was successfully applied to computed tomography scans with low image quality.…”

Section: Denoising Filtersmentioning

confidence: 99%

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Survey on mixed impulse and Gaussian denoising filters

et al. 2020

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This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.

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“…When images are corrupted by salt-and-pepper noise, the respective pixels can take only either a minimum or a maximum value only within the given dynamic range [2]. Restoration of images corrupted by impulse noise is generally restored by many ways using non-linear digital filters [3]. One of the most popular method that was used to remove impulse noise was to use the median filter.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Novel Image Restoration Technique from High Density Impulse Noise through Adaptive Max-Min Filter

Sivakumar*,

Shyam,

Vignesh

et al. 2019

IJITEE

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Noise is ardent factor that reduces the quality of any system, disturbing the corresponding output. This is very noticeable especially in image processing techniques, wherein the image output is distorted to a very large extent. To reduce the effect of noise and to improve the quality of any image, it is mandatory to completely remove high density salt-and-pepper impulse noise. The salt-and –pepper impulse noise is a major hindrance that has to be eliminated by restoring the image using a specialized method that applies to all noise pixels. Involving edge protection and noise control, significant improves image quality compared to those restored by using just a nonlinear filter. The significant feature of the proposed idea is that removal of salt-and-pepper-noise as high as 95%.is obtained.

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An effective hybrid filter for the removal of Gaussian-impulsive noise in computed tomography images

Cited by 11 publications

References 5 publications

Efficient Removal of Noise from Image using Non-Linear Filtering

Efficient Removal of Noise from Image using Non-Linear Filtering

Survey on mixed impulse and Gaussian denoising filters

Implementation of Novel Image Restoration Technique from High Density Impulse Noise through Adaptive Max-Min Filter

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