Infrared Technology, Volume. 47, Issue 3, 299(2025)

A Review of Infrared Image Denoising

Zihui ZHAO1, Yongkang ZHOU1,2, Bangze ZENG1,3、*, Xingfen TANG1, Zhiyu FU1, and Yongcheng YIN1
Author Affiliations
  • 1Kunming Institute of Physics, Kunming 650223, China
  • 2School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
  • 3School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 610050, China
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    The structure of infrared imaging systems and complexity of the imaging environment lead to complex types of noise during infrared image processing, which can seriously affect image quality. This paper first describes the structure of the infrared imaging system and source of image noise, further discussing traditional and improved algorithms for infrared image noise reduction from the perspective of space and frequency domains, air-frequency combination, and deep learning. In this study, we focused on deep learning noise reduction algorithms, in view of their broad application and excellent noise reduction effect. The classical noise reduction algorithm was selected to conduct noise reduction experiments on real noisy infrared images. Experiments show that the deep-learning algorithm surpasses the traditional algorithm in performance.

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    ZHAO Zihui, ZHOU Yongkang, ZENG Bangze, TANG Xingfen, FU Zhiyu, YIN Yongcheng. A Review of Infrared Image Denoising[J]. Infrared Technology, 2025, 47(3): 299

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    Paper Information

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    Received: Dec. 15, 2023

    Accepted: Apr. 18, 2025

    Published Online: Apr. 18, 2025

    The Author Email: ZENG Bangze (zengbangze@163.com)

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