A non-iterative adaptive median filter for image denoising

Bhateja, Vikrant; Rastogi, Kartikeya; Verma, Aviral; Malhotra, Chirag

doi:10.1109/spin.2014.6776932

Cited by 35 publications

(17 citation statements)

References 47 publications

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“…Bhateja et al [20] proposed a method similar to [19]; however, the maximum allowable size of window became 9 × 9. If 9 × 9 window surrounding noisy pixel does not have any known noise-free pixel, it replaces the noisy pixel with previously processed or previous noise-free pixel.…”

Section: <mentioning

confidence: 99%

“…Performance evaluation was made on the basis of comparison of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM) of adaptive DWMF with methods proposed by adaptive median filter (AMF), adaptive weighted median filter (AWMF), Ibrahim et al [19] (AdFuz), Bhadouria [21], and Bhateja et al [20]. [20]. e SAMF.…”

Section: Statistics Calculation For Adaptive Criteriamentioning

confidence: 99%

“…SAMF among conventional methods performs well for low-density noise; however, for higher densities, it provides artifacts along edges. For 90% noise density, AMF and AWMF fail to remove noise, whereas SAMF, Bhadouria [20], and Bhateja [21] restore all noisy pixels, however, at the cost of blurring along edges. The proposed method not only preserves edges for low density of noise but also performs very well for noise density as high as 90%.…”

Section: Statistics Calculation For Adaptive Criteriamentioning

confidence: 99%

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An adaptive dynamically weighted median filter for impulse noise removal

Khan

Lee

2017

EURASIP J. Adv. Signal Process.

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A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

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Section: <mentioning

confidence: 99%

Section: Statistics Calculation For Adaptive Criteriamentioning

confidence: 99%

Section: Statistics Calculation For Adaptive Criteriamentioning

confidence: 99%

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An adaptive dynamically weighted median filter for impulse noise removal

Khan

Lee

2017

EURASIP J. Adv. Signal Process.

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“…Internal denoising algorithms use larger patches and search-windows at higher noise levels [13][14][15][16], improving reconstructed quality. Extensions to the NLM algorithm proposed adaptive spatial support for superior results, by classifying patches as textured or smooth by edge-detection and morphological operations [17], or by clustering the SVD (singular-value decomposition) of the blocks' gradient fields [18], allowing spatial adaptation for each block.…”

Section: Related Workmentioning

confidence: 99%

A largest matching area approach to image denoising

Gaston

Crookes

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…One approach to improve quality of ultrasound images is to suppress speckle noise without affecting important features and texture of the image. Therefore, previous studies have attempted to address the challenges by proposing restoration and enhancement methods [1], [8]- [14]. And, the classical noise-suppressing frameworks for multilicative noise removal are total variation [15]- [23] wavelet [24]- [31] and linear/nonlinear diffusion [9], [32]- [35].…”

Section: Introductionmentioning

confidence: 99%