FLANN Detector Based Filtering of Images Corrupted by Impulse Noise

Majhi, Banshidhar; Fathi, Mowafak

doi:10.3844/jcssp.2005.332.336

Cited by 4 publications

(1 citation statement)

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“…Vijaykumar et al (2008) proposed a robust estimation based filter, which reduces the streaking effect but for higher noise densities the image gets blurred. Majhi and Fathi (2005) proposed a novel method based on neurodetector using functional link artificial neural network. The neural detector is based on the concept of training and it detects the impulse noise efficiently, however the training method must be precisely done.…”

Section: Jcsmentioning

confidence: 99%

Removal of High Density Impulse Noise Using Morphological Based Adaptive Unsymmetrical Trimmed Mid-Point Filter

Saravanakumar¹,

Jeyakumar²,

Vijeyakumar³

2014

Journal of Computer Science

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A Morphological based Adaptive Unsymmetrical Trimmed Mid-Point Filter (MAUTMPF) for the restoration of gray scale images corrupted by salt and pepper noise for varying noise densities is proposed in this study. Images corrupted by impulsive noise severely hinder subsequent image processing tasks, such as edge detection, image segmentation, object recognition, etc. Therefore, it is absolutely essential to restore the original image from the corrupted image. The proposed algorithm replaces the corrupted pixel by mid point value out of the retained pixels other than 0's and 255's in a 3×3 window. The essential condition for the validity of the window is that at least two pixels in the selected window should be uncorrupted; if not the window size is incremented by 2. The iteration stops when the window size reaches 7. In particular case, when the condition for validity doesn't hold in 7×7 window then the original 3×3 window is chosen and midpoint of minimum and maximum values of already processed pixels is replaced with the centre pixel. experimental evaluation using MATLAB reveals that our MAUTMPF shows better performance compared to the previous de-noising algorithms in terms of Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) for noise densities up to 90%. The validity of the proposed algorithm is verified by testing it for different gray scale images.

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Section: Jcsmentioning

confidence: 99%

Removal of High Density Impulse Noise Using Morphological Based Adaptive Unsymmetrical Trimmed Mid-Point Filter

Saravanakumar¹,

Jeyakumar²,

Vijeyakumar³

2014

Journal of Computer Science

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ECG signal denoising by Functional Link Artificial Neural Network (FLANN)

Dey

Dash²,

Dash

2011

IJBET

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An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

Jena

Patel²,

Sinha³

2022

Research Anthology on Artificial Neural Network Applications

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A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

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FLANN Detector Based Filtering of Images Corrupted by Impulse Noise

Cited by 4 publications

References 0 publications

Removal of High Density Impulse Noise Using Morphological Based Adaptive Unsymmetrical Trimmed Mid-Point Filter

Removal of High Density Impulse Noise Using Morphological Based Adaptive Unsymmetrical Trimmed Mid-Point Filter

ECG signal denoising by Functional Link Artificial Neural Network (FLANN)

An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

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