Open AccessLow-illumination image enhancement algorithm based on multi-feature fusion
Author(s) -
Xuewei Zhang,
Xiaojuan Sun,
Tianfeng Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/799/1/012038
Subject(s) - artificial intelligence , computer vision , histogram equalization , computer science , feature (linguistics) , rgb color model , brightness , image fusion , image (mathematics) , feature detection (computer vision) , gamma correction , histogram , adaptive histogram equalization , image enhancement , pattern recognition (psychology) , image processing , optics , linguistics , philosophy , physics
In order to improve the effect of image acquisition under the condition of low illumination, a low illumination image enhancement algorithm based on multi-feature fusion is adopted in this paper. In this algorithm, the illumination information is extracted by bilateral filtering, and the illumination parameters are processed by adaptive gamma correction and limited contrast histogram equalization algorithm. The final illumination parameters are obtained by the feature weighted fusion of the processed parameters and the original illumination parameters. The RGB image is obtained by combining the illumination and reflection parameters, and the enhancement of the image is realized. The experimental results show that the algorithm used in this paper can not only improve the brightness of the image, but also enhance the characteristic parameters of the image, remove the noise, and make the color information of the image fuller and richer