Ms. Jinsha Wu Profile

Jinsha Wu

at Xidian Univ

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Proceedings Article | 24 October 2017 Paper

Kernel regression based infrared image non-uniformity correction

Jinsha Wu, Hanlin Qin, Ying Liang, Huixin Zhou, Qingjie Zeng, Runda Qian

Proceedings Volume 10462, 1046255 (2017) https://doi.org/10.1117/12.2285722

KEYWORDS: Infrared imaging, Nonuniformity corrections, Infrared radiation

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In traditional scene-based non-uniformity correction methods, ghosting artifacts and image blurring affect the response uniformity of the infrared focal plane array imaging system seriously and decrease the image quality. In order to suppress artifacts ghosting and improve image quality, this paper proposed a new based on kernel regression nonuniformity correction method for infrared image, because of its powerful ability to estimating. The main purpose of proposed method is to obtain reliable estimations of gain and offset parameters. Firstly, in order to suppress the ghost artifacts normally introduced by the strong edge effectively, this paper employs the kernel regression method to estimate the desired pixel value of each detector uint. Then the two correction parameters are achieved with the steepest descent method for the purpose of updating these two parameters synchronously. Finally, more accurate estimations of the two correction parameters can be obtained. Several simulated infrared image sequences are utilized to verify the performance of the proposed method. The results show that our method performs better than other compared methods.

Proceedings Article | 24 October 2017 Paper

Temporal high-pass filter non-uniformity correction based on guided bilateral filter for IRFPA

Ying Liang, Hanlin Qin, Jinsha Wu, Huixin Zhou, Qingjie Zeng

Proceedings Volume 10462, 104624C (2017) https://doi.org/10.1117/12.2285514

Read Abstract +

In order to solve the problem of ghost artifacts in the traditional nonuniformity correction(NUC) method, a new scene-based guided bilateral filter(GBF) nonuniformity correction was proposed. In this paper, the original input image sequences are processed by the guided bilateral filter firstly, then the expected output imagine with the boundary information was estimated recursively only by using high spatial-frequency part of the image which contains most of the noise and nonuinformity. The method was verified with several infrared image sequences, and several experimental results show that the proposed method can significantly reduce the ghosting artifacts in temporal high-pass filter(THPF) and achieve a better nonunifotmity correction effect.

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