Variational Contrast Enhancement of RGB Images

Abstract : The aim of this paper is twofold. First, we propose a new method for enhancing the contrast of gray-value images. We use the difference of the average local contrast measures between the original and the enhanced images within a variational framework. This enables the user to control the contrast level and the scale of the enhanced details intuitively. Moreover, our model avoids large modifications of the original image histogram. Thereby it preserves the global illumination of the scene and can cope with large areas having similar gray values. The minimizer of the proposed functional is computed by a gradient descent algorithm in connection with a polynomial approximation of the average local contrast measure. The polynomial approximation is done via Bernstein polynomials and leads to to a speed up of the algorithm by applying fast Fourier transforms. In the second part, the approach is extended to a variational enhancement method for color images. The model approximately preserves the hue of the original image and includes additionally a total variation term to correct possible noise. The method requires no post-or pre-processing. The minimization problem is solved with a hybrid primal-dual algorithm. Numerical experiments demonstrate the efficiency and the flexibility of the proposed approaches in comparison with state-of-the-art methods.
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Fabien Pierre, Jean-François Aujol, Aurélie Bugeau, Gabriele Steidl, Vinh-Thong Ta. Variational Contrast Enhancement of RGB Images. 2015. ⟨hal-01237038v4⟩

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