IEEE Trans Image Process - Vector-valued image processing by parallel level sets.

Tópicos

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{ imag(2830) propos(1344) filter(1198) }
{ method(1219) similar(1157) match(930) }
{ problem(2511) optim(1539) algorithm(950) }
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{ perform(999) metric(946) measur(919) }
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{ imag(1057) registr(996) error(939) }
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{ age(1611) year(1155) adult(843) }
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{ sampl(1606) size(1419) use(1276) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Vector-valued images such as RGB color images or multimodal medical images show a strong interchannel correlation, which is not exploited by most image processing tools. We propose a new notion of treating vector-valued images which is based on the angle between the spatial gradients of their channels. Through minimizing a cost functional that penalizes large angles, images with parallel level sets can be obtained. After formally introducing this idea and the corresponding cost functionals, we discuss their G?teaux derivatives that lead to a diffusion-like gradient descent scheme. We illustrate the properties of this cost functional by several examples in denoising and demosaicking of RGB color images. They show that parallel level sets are a suitable concept for color image enhancement. Demosaicking with parallel level sets gives visually perfect results for low noise levels. Furthermore, the proposed functional yields sharper images than the other approaches in comparison.

Resumo Limpo

vectorvalu imag rgb color imag multimod medic imag show strong interchannel correl exploit imag process tool propos new notion treat vectorvalu imag base angl spatial gradient channel minim cost function penal larg angl imag parallel level set can obtain formal introduc idea correspond cost function discuss gteaux deriv lead diffusionlik gradient descent scheme illustr properti cost function sever exampl denois demosaick rgb color imag show parallel level set suitabl concept color imag enhanc demosaick parallel level set give visual perfect result low nois level furthermor propos function yield sharper imag approach comparison

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