Level-base compounded logarithmic curve function for colour image enhancement

Abstract

In this study, the authors present a new strategy to implement an illumination compensation-based contrast enhancement. Different from the traditional pixel-to-pixel transformation, the proposed method offers a level-to-level framework by generating the reference intensity level and the given target intensity level. Fundamentally, the traditional illumination compensation algorithms such as Histogram equalisation, log and gamma transformation are trade-off strategies and face the same dilemma. For example, the log function compresses the dynamic range of image with large variations in pixel values. The proposed method is an integration algorithm, the illumination compensation could be considered as the transformation from reference intensity to the chosen target intensity, and the contrast is enhanced by the given algebraic definition of the characteristic values of each pixel. The advantages are the adjusted intensity would be accomplished in the native phase according to the variant characteristic intensity values, clarifying the details of the darker areas as well as preserving the highlight areas, reducing HALO, colour constancy, removing colour cast and colour restoration. Shown as the experimental results, the proposed method can obtain better performance compared with other methods in subjective evaluation with contour plot and the objective evaluations by universal quality index, structural similarity index and average of standard deviations.

Visual illumination compensation for face images using light mapping matrix

“…Many approaches are proposed to perform illumination normalisation in the past couple of decades. Conventional algorithms such as histogram equalisation [1,2], logarithmic transformation [2][3][4] and contrast modification [5][6][7] are widely used in computer vision and image processing for image enhancement. Jobson et al [8] propose single-scale retinex (SSR) approach based on reflectance-illumination model which enhances image by improving the local contrast.…”