Abstract
Nonuniform illumination images suffer from degenerated details because of underexposure, overexposure, or a combination of both. To improve the visual quality of color images, underexposure regions should be lightened, whereas overexposure areas need to be dimmed properly. However, discriminating between underexposure and overexposure is troublesome. Compared with traditional methods that produce a fixed demarcation value throughout an image, the proposed demarcation changes as local luminance varies, thus is suitable for manipulating complicated illumination. Based on this locally adaptive demarcation, a nonlinear modification is applied to image luminance. Further, with the modified luminance, we propose a nonlinear process to reconstruct a luminance-enhanced color image. For every pixel, this nonlinear process takes the luminance change and the original chromaticity into account, thus trying to avoid exaggerated colors at dark areas and depressed colors at highly bright regions. Finally, to improve image contrast, a local and image-dependent exponential technique is designed and applied to the RGB channels of the obtained color image. Experimental results demonstrate that our method produces good contrast and vivid color for both nonuniform illumination images and images with normal illumination.
Highlights
In real life, the number of light sources is limited and various objects can block out lights, light is usually accompanied by shadows
These algorithms can be roughly categorized as algorithms based on the retinex theory,[1] methods that apply nonlinear modification to image luminance, and algorithms that are devised in some transformed space
Comparisons have been made with some classic algorithms, including the multiscale retinex algorithm MSRCR,[10] RACE,[12] which is a locally fused version of RSR5 and ACE11 algorithms, and an algorithm[20] that used Naka–Rushton formula for tone mapping
Summary
The number of light sources is limited and various objects can block out lights, light is usually accompanied by shadows. The enhancing effect of RSR is not obvious when the image contains highly-bright pixels These pathwise retinex[1,2,3,4] and RSR5 algorithms select a set of neighbors and use the local maximum value as the referenced white points. Different from the globally fixed demarcation value used by Tao et al.[15] and Choudhury and Medioni,[19] the proposed demarcation changes as local luminance varies, adapting to complicated luminance and helping to control the enhancement degree for various regions in an image According to this adaptive demarcation, local luminance is modified by SNRF to depress highly bright areas and light up dark areas.
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