Low-light enhancement based on an improved simplified Retinex model via fast illumination map refinement

• Published in: Pattern analysis and applications : PAA Vol. 24; no. 1; pp. 321 - 332
• Main Authors: Hao, Shijie, Han, Xu, Zhang, Youming, Xu, Lei
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2021; Springer Nature B.V
• Subjects: Computer Science; Illumination; Image enhancement; Image processing; Light; Lighting; Noise reduction; Pattern Recognition; Short Paper; Image contrast enhancement; Illumination map refinement; Simplified retinex model
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-020-00908-2

Low-light enhancement is an important post-image-processing technique, as it helps to reveal hidden details from dark image regions. In this paper, we propose a fast low-light enhancement model, which is robust to various lighting conditions and imaging noise, and is computationally efficient. By using a fusion-based simplified Retinex model, our model caters to different lighting conditions. In the model, we propose an edge-preserving filter to efficiently refine the estimated illumination map. We also extend our model by equipping it with a very simple denoising step, which effectively prevents the over-boosting of imaging noise in the dark regions. We conduct the experiments on public available images as well as the ones collected by ourselves. Visual and quantitative results validate the effectiveness of our model.