Low-light image enhancement using inverted image normalized by atmospheric light

Abstract

Images captured under low-light conditions are characterized by problems, including low brightness, low contrast, and color distortion. Low-light image enhancement, aims to enhance visual effects for the benefit of subsequent image processing and computer vision tasks, but it presents a challenging problem. This paper proposes an efficient and fast algorithm for low-light image enhancement. First, it derives a mathematical connection between the Retinex theory and the atmospheric scattering model by normalizing an inverted image to atmospheric light. Next, medium transmission is derived as a function of the saturation of the scene radiance only, and the image-adaptive saturation of scene radiance is estimated via a simple stretch function according to the average saturation of the low-light image. The proposed algorithm is fast because it requires no training, prior knowledge, or refinement. The simulation results confirm that in terms of both computational complexity and enhancement efficiency, the proposed low-light enhancement scheme outperforms other state-of-the-art approaches.