Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

Chan, Raymond H.; Ho, Chun Hok; Nikolova, Mila

doi:10.1109/tip.2005.852196

Cited by 947 publications

(584 citation statements)

References 20 publications

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“…This is an attempt in direction where huge legacy of developed digital image processing methods can be used for a different purpose with respect to the original one. Raymond H.Chan et all., [14]In this paper, we propose a powerful two-stage scheme which combines the variational method proposed with the adaptive median filter [5]. More precisely, the noise candidates are first identified by the adaptive median filter, and then these noise candidates are selectively restored using an objective function with an data-fidelity term and an edgepreserving regularization term.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

A Comparative of Analysis of Image Restoration Using Mean and Median Filtering

Joice¹

2017

IJRASET

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Image restoration is an important area to better the quality of an image, that attempts to recreate or remodel an image from noise. Image noise is a variation of brightness or unwanted signals and it used to destroy most of the part of image. Image destroyed by various type of noise such as Gaussion noise, Salt and Pepper noise, Poisson noise, etc., Several techniques are used to remove the noise and modifying or restore an image without resolution loss. In image restoration the operation implemented with filtering include smoothing, sharpening, edge detection. This paper is concentrated for analyzing the denoising of salt and pepper noise using filtering techniques.

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Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

A Comparative of Analysis of Image Restoration Using Mean and Median Filtering

Joice¹

2017

IJRASET

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“…A critical parameter was the SAM maximum angle field, to define which pixel may (or may not) be classified as OOMW areas. Based on the spectral profiles from in-situ observations [34], it was found that OOMW disposal areas tend to have low reflectance values in the visible part of the spectrum (ρ < 5%) and relative high reflectance values in the near infrared part of the spectrum (ρ = 10%). Thus, a minimum SAM angle was decided using these thresholds.…”

Section: Case Study 1: Mesaras Inletmentioning

confidence: 99%

Detection of olive oil mill waste (OOMW) disposal areas using high resolution GeoEye’s OrbView-3 and Google Earth images

2016

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Abstract:The olive oil industry is considered to be as one of the driving sectors of the agricultural economy of the Mediterranean basin. The extraction of olive oil generates huge quantities of wastes that may have a great impact on land and water environments due to high concentrations in phenolic compounds that could cause ophytotoxicity. This paper aims to examine the potential use of freely distributed satellite images for the detection of olive oil mil waste (OOMW) areas in the island of Crete through the use of two cases studies. In the first case study an archive GeoEye OrbView-3 image was used to detect OOMW areas using the Spectral Angle Mapper detection algorithm and other geometric and topographic parameters. In the second case study, Google Earth images were examined through different classification algorithms at different scales. The overall results demonstrate that remote sensing techniques can be used as an alternative to field observations so as to detect and monitor OOMW areas Furthermore, freely distributed RGB images from digital globes (such as Google Earth) can be sufficiently and effectively used for this purpose.

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“…This noise appears owing to the presence of minute grey scale variations within the image. Median filtering could be a widespread technique of the image improvement for removing noise without effectively reducing the image sharpness [10].…”

Section: Types Of Filters 41 Median Filtermentioning

confidence: 99%

Reduction of types of Noises in dental Images

Subbuthai¹,

Kavithabharathi²,

Muruganand³

2013

IJCATR

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Abstract---This paper presents a filter for restoration of Dental images that are highly corrupted by salt and pepper noise and speckle noise, Poisson noise. After detecting and correcting the noisy pixel, the proposed filter is able to suppress noise level. In this paper for each noise proposed different type of filter and compare these three types of filter with their PSNR value and MSE value and SNR value. After filtering stage maximum detected noise pixels will be filtered and simulation results show the filtered image.

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Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

Cited by 947 publications

References 20 publications

A Comparative of Analysis of Image Restoration Using Mean and Median Filtering

A Comparative of Analysis of Image Restoration Using Mean and Median Filtering

Detection of olive oil mill waste (OOMW) disposal areas using high resolution GeoEye’s OrbView-3 and Google Earth images

Reduction of types of Noises in dental Images

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