IEEE Trans Image Process - A universal denoising framework with a new impulse detector and nonlocal means.

Tópicos

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Resumo

Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.

Resumo Limpo

impuls nois detect critic issu remov impuls nois impulsegaussian mix nois paper propos new detect mechan univers nois univers noisefilt framework base nonloc mean nlmean oper carri two stage ie detect follow filter detect first propos robust outlying ratio ror measur impulselik pixel pixel divid four cluster accord ror valu second differ decis rule use detect impuls nois base absolut deviat median cluster order make detect result accur robust fromcoarsetofin strategi iter framework use addit detect procedur consist two stage ie coars fine detect stage filter nlmean extend impuls nois introduc refer imag univers denois framework propos combin new detect mechan nlmean rornlm final extens simul result show propos nois detector superior exist detector rornlm produc excel result outperform exist filter differ nois model unlik impuls nois filter propos rornlm also achiev high peak signaltonois ratio great imag qualiti effici remov impulsegaussian mix nois

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