RETRACTED ARTICLE: Removal of high density salt and pepper noise in color image through modified cascaded filter

Karthik, B.; Kumar, T. Krishna; Vijayaragavan, S. P.; Sriram, M.

doi:10.1007/s12652-020-01737-1

Cited by 32 publications

(12 citation statements)

References 20 publications

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“…Noise removal and thresholding techniques are generally used to refine and smooth images from the results of scanning. The median filter can be used to remove Salt and paper noise [45,46] and the Otsu thresholding for image binarization [47,48]. The handwriting pressure feature can be extracted by the grayscale value of the handwriting portion and discards the background portion.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Automated handwriting analysis based on pattern recognition: a survey

Samsuryadi¹,

Kurniawan²,

Mohamad³

2021

IJEECS

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<span>Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. </span>

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Section: Data Collection and Pre-processingmentioning

confidence: 99%

Automated handwriting analysis based on pattern recognition: a survey

Samsuryadi¹,

Kurniawan²,

Mohamad³

2021

IJEECS

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“…When the estimation of the speech is carried out too hard by these methods it may result in the content loss, so further research in this area is required. [5] Selvaraj et al, (2019) suggested a method to enhance the speech signal which shows adverse effect by the non-stationary noises low SNR. Here the sliding window concept is combined with the EMDH algorithm to form the SWEMDH method, which is used to improve the speech enhancement process.…”

Section: Literature Surveymentioning

confidence: 99%

Stuttered Speech Recognition And Classification Using Enhanced Kamnan Filter And Neural Network

Vaidianathan¹,

Subramanian²,

Karthik³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Stuttering or stammering assessment is one of the vital factors in speech recognition algorithms. To reconstruct the stuttered speech into spontaneous speech it is necessary to detect and correct the features influencing the speech signal. In this paper the speech signal is processed based on the disturbances created by acoustic effects like pauses and noises made both externally and internally. To eliminate the effects of noise on speech signal an Enhanced Kalman Filter is introduced here and its performance along with various filters are studied and compared based on the parameters like Mean Square Error (MSE), Mean Absolute Error (MAE), SNR ratio, Peak Signal to Noise ratio and Cross correlation. Then based on the extracted features classification of the speech signal is carried out using Convolutional Neural Network (CNN) algorithm of Deep learning technique.

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“…Generally, image noises can be divided into Gaussian noise, Poisson noise, and Salt-and-Pepper (SAP) noise in terms of discreteness and randomness. SAP noise commonly exists in natural images, and the pixels contaminated by SAP noise take the maximum or minimum value and can be represented as black or white points [7,8]. Considering the universality of SAP noise, we focus our attention on the SAP noise and develop a denoising method that provides some help for further image analysis.…”

Section: Introductionmentioning

confidence: 99%

NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise

Zhang

Zhu

Zheng

2021

Mathematical Problems in Engineering

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In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.

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RETRACTED ARTICLE: Removal of high density salt and pepper noise in color image through modified cascaded filter

Cited by 32 publications

References 20 publications

Automated handwriting analysis based on pattern recognition: a survey

Automated handwriting analysis based on pattern recognition: a survey

Stuttered Speech Recognition And Classification Using Enhanced Kamnan Filter And Neural Network

NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise

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