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. 2019 Dec 6;19(24):5395.
doi: 10.3390/s19245395.

Statistical Scene-Based Non-Uniformity Correction Method With Interframe Registration

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Free PMC article

Statistical Scene-Based Non-Uniformity Correction Method With Interframe Registration

Baolin Lv et al. Sensors (Basel). .
Free PMC article

Abstract

The non-uniform response in infrared focal plane array (IRFPA) detectors inevitably produces corrupted images with a fixed-pattern noise. In this paper, we present a novel and adaptive scene-based non-uniformity correction (NUC) method called Correction method with Statistical scene-based and Interframe Registration (CSIR) , which realizes low delay calculation of correction coefficient for infrared image. This method combines the statistical method and registration method to achieve a better NUC performance. Specifically, CSIR estimates the gain coefficient with statistical method to give registration method an appropriate initial value. This combination method not only reduces the need of interactive pictures, which means lower time delay, but also achieves better performance compared to the statistical method and other single registration methods. To verify this, real non-uniformity infrared image sequences collected by ourselves were used, and the advantage of CSIR was compared thoroughly on frame number (corresponding to delay time) and accuracy. The results show that the proposed method could achieve a significantly fast and reliable fixed-pattern noise reduction with the effective gain and offset.

Keywords: adaptive; fixed-pattern noise; non-uniformity correction; registration method; statistical scene.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scheme of the proposed statistical scene-based non-uniformity correction method with interframe registration (SIR).
Figure 2
Figure 2
Diagram for calculating the gain coefficients using whole image.
Figure 3
Figure 3
Diagram of modular pixel consistency calculation.
Figure 4
Figure 4
Pixel value distribution contrast map before and after CSIR NUC. (a) pixel value distribution contrast map before CSIR; (b) pixel value distribution contrast map after CSIR; (c) corresponding picture before CSIR; (d) corresponding picture after CSIR.
Figure 4
Figure 4
Pixel value distribution contrast map before and after CSIR NUC. (a) pixel value distribution contrast map before CSIR; (b) pixel value distribution contrast map after CSIR; (c) corresponding picture before CSIR; (d) corresponding picture after CSIR.
Figure 5
Figure 5
NUC performance comparison of representative frame of the three test sequences: (1,5,9) original images; (2,6,10) two-point method; (3,7,11) IRLMS method; and (4,8,12) proposed SIR method.
Figure 6
Figure 6
Corrected images corresponding to different F frames.

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