Adaptive Global and Local Contrast Enhancement

Abstract

Brightness and contrast are very important characteristics of a photo. Images with exposure issues with low global and local contrast should be corrected before printing. The purpose of correction consists of making photos more pleasing to the observer. We consider techniques for automatic global contrast adjustment, improvement of dark and light areas of a photo, increasing local contrast for visibility enhancement, and dehazing. In addition, we apply modification of colour channels to preserve the saturation. Backlighting leads to poorly distinguishable details in shadow areas. Our approach to correction of the dark areas is based on contrast stretching and alpha-blending of the brightness of the initial image and estimated reflectance. We use a simple model of illumination for estimation of reflectance. Luminance is the outcome of filtering by a bilateral filter (BF). Reflectance is the ratio between the brightness of the initial image and estimation of the luminance. We train a regression model by the random forest technique for adaptive calculation of a correction parameter. Features for machine learning are extracted from a global brightness histogram. Also, we describe a method of visibility enhancement. The algorithm carries out locally adaptive tone mapping by means of a flexible S-shaped curve. We use a cubic Hermit spline as an S-shaped tone mapping function. The starting and ending points of the spline depend on global brightness contrast, whereas tangents depend on the local distribution of background and foreground pixels. Alteration of the tangents for adjacent areas is smoothed to avoid the formation of visible artefacts. The technique is applicable for the correction of images with fog and smoke as well as underwater photos.