Histogram warping is a novel histogram specification technique for use in color image processing. As a general purpose tool for color correction, our technique constructs a global color mapping function in order to transform the colors of a source image to match a target color distribution to any desired degree of accuracy. To reduce the risk of color distortion, the transformation takes place in an image dependent color space, featuring perceptually uniform color axes with statistically independent chromatic components. Eliminating the coherence between the color axes enables the transformation to operate independently on each color axis. Deforming the source color distribution to reproduce the dominant color features of the target distribution, the histogram warping process is controlled by designating the color shifts and contrast adjustments for a set of key colors. Assisted by mode detection, matching quantiles establish the correspondence between the color distributions. Interpolation by monotonic splines serves to extend the mapping over the entire dynamic range without introducing artificial discontinuities into the resulting color density. We show how our method can be applied to color histogram equalization as well as color transfer from an example image or a color palette.

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Color histogram specification by histogram warping

We present an automated algorithm for global contrast enhancement of images with multimodal histograms. To locate modes and valleys, histogram analysis is performed by kernel density estimation, a robust nonparametric statistical method. Histogram warping by monotonic splines pushes the modes apart, spreading them out more evenly across the dynamic range. This technique can assist in the contrast correction of images taken facing the light source.

/pdf/automatic-contrast-enhancement-by-histogram-warping-dze3b35j9g.pdf

Automatic contrast enhancement by histogram warping

We implement contrast brushes, an interactive method for directly brushing contrast adjustments onto an image. The adjustments are performed by a histogram warping approach that implements tone mapping using piecewisedefined, continuously differentiable, monotonic splines. This allows the independent specification of tone changes and contrast adjustments without causing halo or contouring artifacts, while still endowing contrast brushes with intelligible parameters that render their effects predictable for the user. A user study demonstrates that contrast brushes can prove more effective than Adobe Photoshop's interactive contrast enhancement tools.

Contrast brushes: interactive image enhancement by direct manipulation

It is shown that the calculations widely used to perform histogram equalization are not guaranteed to yield images which optimally utilize the intensity range. A refinement of these calculations is presented which ensures optimal intensity range utilization.

A note an Histogram equalization for optimal intensity range utilization

Histogram equalization is a simple and effective method for image contrast enhancement as it can automatically define the enhancement transformation function based on the image content. However, it tends to change the global appearance of the processed image by shifting its mean brightness toward the middle of the graylevel range, thus making it unsuitable for some applications, such as consumer electronics. In this paper we propose a new method referred as Constrained Variational Histogram Equalization, which builds on the variational formulation of histogram equalization to automatically define an optimal transformation function that would compromise between contrast enhancement and the preservation of the image mean brightness. Experimental evaluation proved this new method to outperform the classical histogram equalization method.

Image contrast enhancement by constrained variational histogram equalization

Image enhancement techniques are described for increasing visual contrast in an intensity range (or ranges) of interest. Enhancement is accomplished by histogram transformation where (except for quantization error) there is no change in entropy between the input and output images. This preservation of entropy is desired for some applications where it is imperative that no artifacts be introduced - as may result from such enhancement techniques as spatial filtering and adaptive histogram transformation. The contribution of this work is toward the compilation and organization of useful image enhancement techniques and toward an understanding of matching particular transformations with the desired purposes. Two types of methods for obtaining histogram transformations are distinguished here. In one type the output histogram is specified as a ramp-shaped function, and the transformation is found. This type is useful for automatically enhancing low or high intensity ranges, and for histogram equalization. For the second type, the general form of the transformation function is defined to be a third order polynomial function, which is specified by the choice of the intensity region of interest and the desired amount of contrast enhancement. This type is useful for interactive enhancement. Examples are shown which illustrate where each of the transformations is applicable, and what results are obtained from such transformations.

Lawrence O' Gorman

Papers

Entropy-Constant Image Enhancement by Histogram Transformation