A Survey of Uncooled Infrared Image Denoising Algorithms

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

Volume 43 Issue 6

Jun. 2021

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Article Navigation > Infrared Technology > 2021 > 43(6): 557-565

WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.

Citation:

WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.

WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.

Citation:

WANG Jia, ZHOU Yongkang, LI Zemin, WANG Shijin, ZENG Bangze, ZHAO Deli, HU Jianchuan. A Survey of Uncooled Infrared Image Denoising Algorithms[J]. Infrared Technology , 2021, 43(6): 557-565.

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A Survey of Uncooled Infrared Image Denoising Algorithms

1.
Kunming Institute of Physics, Kunming 650223, China
2.
The First Military Representative Office in Kunming of the Army Department in Chongqing, Kunming 650032, China

Received Date: 2020-06-22
Rev Recd Date: 2020-07-28
Publish Date: 2021-06-20

Abstract

Abstract

In infrared image processing, owing to technical issues with the infrared detector, the original infrared image includes a variety of noise, especially salt and pepper noise, fixed noise, or random stripe noise. Currently, there are many filtering algorithms for infrared image denoising, but they emphasize time, space, denoising effect, maintaining detail, and so on differently; therefore, it is difficult to achieve a perfect combination. Identifying methods to filter noise information more quickly, efficiently, and accurately and retain more details is an important future research direction for noise reduction in infrared image processing. This study investigated and compared the current mainstream infrared image denoising algorithms from three categories: traditional filter denoising, transform domain filter denoising, and image layered processing filter denoising, and a combination of a traditional algorithm and image layered adaptive denoising algorithm is proposed to provide a reference for future studies in related fields.
- infrared image noise reduction,
- adaptive noise reduction,
- traditional filter,
- image layered filter,
- transform domain filter

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References(21)

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