Improved adaptive spatial distance-weighted median filter

Jin, Lianghai; Li, Dehua; Lu, Zijian

doi:10.1117/1.2713400

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…Some prior-art methods, such as the classical SMF, 2 ROMF, 17 TSMF, 10 CWMF ͑W c =3͒, 6 AEWMF, 7 VWTMF, 16 AWMF, 8 ADWMF, 9 Chen and Wu's method ͑Chen͒ 15 ͑with parameters s = 0.3 and ͓␦ 0 , ␦ 1 , ␦ 2 , ␦ 3 ͔ = ͓55, 40, 25, 15͔͒, and the recent method SAWMF, 20 are used for performance comparison. In the case of filters that require some tuning parameters, those parameters take the optimal values suggested in their original papers.…”

Section: Resultsmentioning

confidence: 99%

“…5 To tradeoff detail preservation against noise reduction, weighted median and switching median filters are proposed. The weighted median filters ͑WMFs͒, 5 such as the center-weighted median filter ͑CWMF͒, 6 the adaptive entropy-weighted median filter ͑AEWMF͒, 7 the adaptive weighted median filter based on impulsive noise detection ͑AWMF͒, 8 and the adaptive distance-weighted median filter ͑ADWMF͒, 9 can perform different amounts of smoothing according to different weighting coefficient sets. The switching median filters, such as the tristate median filter ͑TSMF͒, 10 the linedetection-based switching median filter, 11 the noise adaptive soft-switching median filter, 12 the progressive switching median filter, 13 the multistate median filter, 14 and the CWMF-based switching median filter, 15 first utilize some impulsive noise detectors to classify the image pixels into corrupted and noise-free ones, and then apply robust filtering operations only in the detected noisy positions to suppress noise and keep the noise-free pixels unchanged.…”

Section: Introductionmentioning

confidence: 99%

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Impulsive noise removal using switching scheme and adaptive weighted median filters

et al. 2010

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We present a new impulsive noise removal method, which combines a switching mechanism and adaptive weighted median filtering techniques. By utilizing four Laplacian operators and median-based comparison techniques to classify the image pixels into thin-line pixels, noise-free pixels, and noisy ones, the developed solution applies adaptive weighted median filtering operations only in the detected noisy locations to suppress impulsive noise, and keeps the other pixels unchanged. The simulation results exhibit the excellent performance gains of the proposed solution in suppressing impulsive noise with different contamination ratios over other prior-art methods in terms of both objective measurements and visual image quality.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impulsive noise removal using switching scheme and adaptive weighted median filters

et al. 2010

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“…Based on the conclusion that the relationship between distances measured in physical unit and perception is generally exponential [15,16], we present a basic weighting function as follows:…”

Section: Basic Weighting Functionmentioning

confidence: 99%

Combining vector ordering and spatial information for color image interpolation

Jin

Song

2009

Image and Vision Computing

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“…The self-adaptive centre weighted median filter proposed by Jin et al [2] can smooth self-adaptively per noise scale. Gao and Gao [3] used PCNN to identify a salt-and-pepper point, then applied a median filter accordingly to the impulsive positions.…”

Section: Introductionmentioning

confidence: 99%

Natural Image-Orientated Hybrid Filter Using Pulse Coupled Neural Network

Pan

2016

Electronics, Communications and Networks V

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Noise introduced during capture and transmission is inevitable for natural images generated by Complementary Metal-Oxide-Semiconductor (CMOS) sensors, including quantization error during digitalization, transmission disturbance and other sources of noise. To process natural images from a CMOS sensor, a hybrid filter combining Pulse Coupled Neural Network (PCNN), median filter and Wiener filter is proposed in this paper. First, salt-and-pepper noise is located via PCNN, and processed by a median filter. Then, Gaussian noise is removed by a self-adaptive Wiener filter. Simulation results indicated that compared to other methods (hybrid filter containing median and Wiener filter, hybrid filter containing median and wavelet filter), the hybrid filter with PCNN demonstrates better performance in the preservation of image detail and edge in the premise of similar Signal-Noise Ratios (SNRs).

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Improved adaptive spatial distance-weighted median filter

Cited by 12 publications

References 10 publications

Impulsive noise removal using switching scheme and adaptive weighted median filters

Impulsive noise removal using switching scheme and adaptive weighted median filters

Combining vector ordering and spatial information for color image interpolation

Natural Image-Orientated Hybrid Filter Using Pulse Coupled Neural Network

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