Infrared Image Denoising Algorithm Based on a Rough Set Approach

WANG Jia; ZHOU Yongkang; HU Jianchuan; PAN Chao; LI Zemin; ZENG Bangze; ZHAO Deli

Volume 43 Issue 1

Jan. 2021

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WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. Infrared Technology , 2021, 43(1): 44-50.

Citation:

WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. Infrared Technology , 2021, 43(1): 44-50.

WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. Infrared Technology , 2021, 43(1): 44-50.

Citation:

WANG Jia, ZHOU Yongkang, HU Jianchuan, PAN Chao, LI Zemin, ZENG Bangze, ZHAO Deli. Infrared Image Denoising Algorithm Based on a Rough Set Approach[J]. Infrared Technology , 2021, 43(1): 44-50.

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Infrared Image Denoising Algorithm Based on a Rough Set Approach

Kunming Institute of Physics, Kunming 650223, China

Received Date: 2020-11-03
Rev Recd Date: 2020-12-30
Publish Date: 2021-01-20

Abstract

Abstract

With respect to the complexity and variety of infrared image noise, it is necessary to consider the detailed enhancement of images while suppressing noise. Accordingly, this study developed an infrared image denoising algorithm using the rough set theory. Collected infrared images were first layered by guided filtering. Then, further multi-dimensional stratification was conducted using the rough set theory, and output images were obtained through merging and restoration. Compared with a subjective observation and an objective evaluation index, the algorithm was effective in infrared image denoising and helped to enhance weak and small target details. In addition, the algorithm showed low complexity and good real-time performance. It thus has good application prospects in engineering implementations.
- infrared image,
- multi dimensional noise reduction,
- guided filtering,
- rough set,
- image layering

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References(14)

References

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