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

In this work, 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. 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 un-naturalness, and information loss. To address these problems, we proposed our idea. 

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. Last, to re-strain 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, in-crease the richness of the visible edges, retain color consistency and reproduce the color quality and naturalness.