IEEE Trans Image Process - Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

Tópicos

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Resumo

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

Resumo Limpo

paper propos adapt imag equal algorithm automat enhanc contrast input imag algorithm use gaussian mixtur model model imag graylevel distribut intersect point gaussian compon model use partit dynam rang imag input graylevel interv contrast equal imag generat transform pixel gray level input interv appropri output graylevel interv accord domin gaussian compon cumul distribut function input interv take account hypothesi homogen region imag repres homogen silenc set gaussian compon imag histogram gaussian compon small varianc weight smaller valu gaussian compon larger varianc graylevel distribut also use weight compon map input interv output interv experiment result show propos algorithm produc better compar enhanc imag sever stateoftheart algorithm unlik algorithm propos algorithm free paramet set given dynam rang enhanc imag can appli wide rang imag type

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