Decision-based median filter using k-nearest noise-free pixels

Hong, Yi; Wang, Hanli

doi:10.1109/icassp.2009.4959803

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Yi Hong et al (2009) [18] proposed a decision based median filter that replaced each noisy pixel with knearest noise free pixels. Experimental results were taken over four images and shown the effectiveness of this proposed approach.…”

Section: Proposed Methodsmentioning

confidence: 99%

An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF

Kaur¹,

Malik²

2015

JOIG

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Image segmentation and evaluation are awfully not easy but significant tribulations in computer vision. In this article we have presented an improved FCM Watershed Algorithm for image segmentation. In this techniques, Decision based median filter used for noise removal, is best for salt and pepper noise reduction. Secondly, Fuzzy C-Means used for cluster selection and for final segmentation modified watershed segmentation used is integrated with FCM. The aim of this method is reduce the number of segments after proposed method and to overcome problem faced by this method which is over-segmentation and noise sensitivity. Computer simulations verify extensive improvement of this proposed method with existing methods in terms of PSNR, SSIM, CQM, MSE, RMSE and BER. These experimental results computed on MATLAB software with Image Processing toolbox. 1

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Section: Proposed Methodsmentioning

confidence: 99%

An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF

Kaur¹,

Malik²

2015

JOIG

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“…Smoothing non-linear filters are best known filters in this category and median filter is one of them. Median filters [22] can close small gaps, eliminate impulse noise or salt and pepper noise. This filter performs better than average filters for removal for salt and pepper noise [23].…”

Section: Noise Reductionmentioning

confidence: 99%

Handwritten Hindi character recognition: a review

Yadav

Purwar

Mittal³

2018

IET Image Processing

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“…Authors in [6] proposes a novel decision-based median filter that replaces each corrupted pixel with the median value of their k-nearest noise-free pixels. Advantages of the median filter using k-nearest noise-free pixels instead of k-nearest pixels are two facets: first, it guarantees that pixels after being restored must be noisefree, because the median filter operator is executed on noise-free pixels; second, the median filter using k-nearest noise-free pixels adaptively adjusts its window size for each pixel such that the number of noise-free pixels locating in the window increases up to k. To realize it, the median filter using k-nearest noise-free pixels firstly detects noise-free pixels in an image, then replaces each corrupted pixel with the median value of their knearest noise-free pixels.…”

Section: B Median Filtermentioning

confidence: 99%

Iris recognition by using blood vessel segmentation

Patil¹,

Patil²,

Karhe³

2016

IOSR

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Among the various biometrics technologies, iris recognition is the most reliable and accurate biometric identification system available. In this paper a novel framework for recognizing and identifying iris is been proposed. Iroimages are pre-processed to remove noise using median filtering on different image planes separately. GLCM and Gabor wavelet texture features have been used to identify the Iris and classification tree approach is used to classify them. The results are improved by the use of combined GLCM and Gabor features. Speed vector machine (SVM) is used for classification. This is an intelligent system which has the ability to identify retina from photographs of their iris and it provides accurate results in less time. The real time database is prepared for the experimental use. The database contains various iris with various shapes, colours and size. Experiment is carried out with the different Iris of different classes and tested. Iris recognition by using blood vessel segmentation

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Decision-based median filter using k-nearest noise-free pixels

Cited by 9 publications

References 6 publications

An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF

An Image Segmentation Using Improved FCM Watershed Algorithm and DBMF

Handwritten Hindi character recognition: a review

Iris recognition by using blood vessel segmentation

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