A nonuniformity correction algorithm for infrared focal-plane arrays

Zhang, Junju; Xing, Suxia; Chang, Benkang; Qian, Yunsheng; Sun, Liling

doi:10.1117/12.573488

Cited by 1 publication

(1 citation statement)

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“…At the same time, the response performance of detector pixel will drift along with the change of working state in the process of use, which will also bring non-uniformity. The non-uniformity correction (NUC) is carried out on the infrared imaging system by using the one-point correction method [8] . However, the correction method based on the reference source will interrupt the imaging process, while the scene-based NUC method uses the image algorithm to update the image data in real time, and the image transmission process will not be interrupted.…”

Section: Introductionmentioning

confidence: 99%

An adaptive infrared scene correction algorithm based on neighborhood offset update

Bangfan,

Bo,

Peng

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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The infrared imaging system needs non-uniformity correction due to problems such as detector manufacturing process. The traditional correction method based on reference source will interrupt the imaging process. The scene-based correction method uses the algorithm to perform real-time non-uniformity correction update on the image data. The image transmission process will not be interrupted, but the ghost problem is always difficult to be solved. This paper analyzes the causes of ghosting in traditional scene correction algorithms and proposes an adaptive infrared scene correction algorithm based on neighborhood bias update. The algorithm uses the gray value of image pixels in adjacent frames to perform inter-frame motion estimation to avoid the ghosting problem caused by insufficient scene motion in the iterative process of the algorithm. The scene correction algorithm based on neighborhood bias update corrects the non-uniformity of the image. The experimental results show that the algorithm proposed in this paper has good convergence, and can avoid the generation of ghosts and effectively improve the image quality.

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Section: Introductionmentioning

confidence: 99%