LECARM: Low-Light Image Enhancement Using the Camera Response Model

• Published in: IEEE transactions on circuits and systems for video technology Vol. 29; no. 4; pp. 968 - 981
• Main Authors: Ren, Yurui, Ying, Zhenqiang, Li, Thomas H., Li, Ge
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; brightness transform function; Camera response function; Cameras; Color; Computer vision; contrast enhancement; Distortion; Exposure; Histograms; Image color analysis; Image enhancement; Image quality; Light; Lighting; low-light image enhancement; Nonlinear distortion; Parameter estimation; Pixels
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2018.2828141

Low-light image enhancement algorithms can improve the visual quality of low-light images and support the extraction of valuable information for some computer vision techniques. However, existing techniques inevitably introduce color and lightness distortions when enhancing the images. To lower the distortions, we propose a novel enhancement framework using the response characteristics of cameras. First, we discuss how to determine a reasonable camera response model and its parameters. Then, we use the illumination estimation techniques to estimate the exposure ratio for each pixel. Finally, the selected camera response model is used to adjust each pixel to the desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with fewer color and lightness distortions compared with the several state-of-the-art methods.