Low-Illumination Image Enhancement Algorithm Based on a Physical Lighting Model

Low-illumination images are usually taken in non-uniform environmental light, such as extremely dark or bright light or artificial light. The enhancement results achieved by existing techniques are prone to halo artifacts, color unnaturalness, and information loss. To address these problems, we present a physical lighting model that describes the degradation of poor illumination images, in which the environmental light is a point-wise variable and changes with the local light source. As long as the parameters in the model are properly estimated, the low-illumination images can be directly recovered by solving the model. First, the initial environmental light can be considered as the incident component according to the Retinex theory and estimated via a Gaussian surrounding function. Second, the environmental light and light-scattering attenuation rate are iteratively adjusted with the information loss constraint. Finally, to restrain the halo and block effects, the two parameters are refined by the weighted guide filter. The experimental results indicate that the proposed algorithm can improve the appearance of low-illumination images that are captured in different scenes, reveal the details in textured regions with few halo effects, increase the richness of the visible edges, retain color consistency and reproduce the color quality and naturalness.