Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map

Abstract

Recently, low-light image enhancement has attracted much attention. However, some problems still exist. For instance, sometimes dark regions are not fully improved, but bright regions near the light source or auxiliary light source are overexposed. To address these problems, a retinex based method that strengthens the illumination map is proposed, which utilizes a brightness enhancement function (BEF) that is a weighted sum of the Sigmoid function cascading by Gamma correction (GC) and Sine function, and an improved adaptive contrast enhancement (IACE) to enhance the estimated illumination map through multi-scale fusion. Specifically, firstly, the illumination map is obtained according to retinex theory via the weighted sum method, which considers neighborhood information. Then, the Gaussian Laplacian pyramid is used to fuse two input images that are derived by BEF and IACE, so that it can improve brightness and contrast of the illuminance component acquired above. Finally, the adjusted illuminance map is multiplied by the reflection map to obtain the enhanced image according to the retinex theory. Extensive experiments show that our method has better results in subjective vision and quantitative index evaluation compared with other state-of-the-art methods.

An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet

“…The gray level transform algorithm adjusts the image grayscale range. For example, histogram equalization adjusts a gray space originally concentrated in the image to the uniform distribution of all gray areas of the image [3], to a certain extent, improving the image brightness but did not consider the relationship between the pixels and making the enhanced images lack the sense of reality [4].…”