Research Article
Research on Image Enhancement Model Based on Variable Order Fractional Differential CLAHE
@INPROCEEDINGS{10.1007/978-3-030-77569-8_15, author={Guo Huang and Li Xu and Qing-li Chen and Xiu-qiong Zhang and Tao Men and Hong-ying Qin}, title={Research on Image Enhancement Model Based on Variable Order Fractional Differential CLAHE}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Fractional calculus Image enhancement Fractional gradient Variable order Histogram enhancement}, doi={10.1007/978-3-030-77569-8_15} }- Guo Huang
Li Xu
Qing-li Chen
Xiu-qiong Zhang
Tao Men
Hong-ying Qin
Year: 2021
Research on Image Enhancement Model Based on Variable Order Fractional Differential CLAHE
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_15
Abstract
Image visual effects can be enhanced primarily through edge and texture enhancement or contrast enhancement. Image enhancement based on fractional differential can effectively enhance image details such as edge and texture using the weak derivative property of the 0–1-order fractional differential operator. Image enhancement based on gray statistics involves the redistribution of light and dark pixels to enhance the overall contrast of the enhanced image as well as the enlargement of the gray-level dynamic range, thereby improving the visual effect of the image effectively. To enhance the edge and texture information of the image, enhance the contrast of the image effectively, and then improve the visual effect of the image, an image enhancement model based on contrast limited adaptive histogram equalization incorporating a fractional differential operator is proposed. The image enhancement model incorporates a fractional differential operator into the adaptive limited contrast image enhancement model, which can enhance the image contrast and effectively enhance the edge and texture details of the image simultaneously. Experimental results show that the proposed variable-order fractional differential contrast-limited adaptive histogram equalization image enhancement model can significantly improve the contrast of the image compared with the traditional fractional differential image enhancement model; additionally, it can effectively enhance the edge and texture details of the image compared with the traditional image enhancement model, which is based on statistical methods.
