Multi-level image detail enhancement based on guided filtering

Xiangrui Tian; Yinjun Jia; Tong Xu; Jie Yin; Yihe Chen; Jiansen Mao

doi:10.1117/12.3014387

9 January 2024 Multi-level image detail enhancement based on guided filtering

Xiangrui Tian, Yinjun Jia, Tong Xu, Jie Yin, Yihe Chen, Jiansen Mao

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296918 (2024) https://doi.org/10.1117/12.3014387
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Image blur and detail information loss are caused by various factors such as imaging environment and hardware performance, therefore a multi-level image detail enhancement method based on guided filtering is proposed. Firstly, the input image is iteratively filtered by using the guided filter, to obtain background images with different smoothness; then the background image is subtracted from the original image to obtain detail images with different levels; finally, a dynamic saturation function is used to adjust the weights of detail images, which are superimposed with the original image to obtain the enhanced image. The proposed method is compared with the existing enhancement algorithms using open dataset. The experimental results show that, compared with other enhancement methods, the proposed method in this paper achieves a better enhancement effect, the enhanced image has clear edges, and the visual effect is suitable. Compared with other methods, the objective indicators of information entropy, average gradient, and spatial frequency are improved on average. 1.39%, 27.9%, and 19.3%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangrui Tian, Yinjun Jia, Tong Xu, Jie Yin, Yihe Chen, and Jiansen Mao "Multi-level image detail enhancement based on guided filtering", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296918 (9 January 2024); https://doi.org/10.1117/12.3014387

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KEYWORDS

Image enhancement

Image filtering

Tunable filters

Image processing

Image quality

Visualization

Infrared radiation

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