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Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays
Rui Lai1, Yin-tang Yang, Duan Zhou
1Department of Microelectronics, Xidian University, No. 2 South Taibai Road, Xi'an, Shannxi 710071, China. rlai@mail.xidian.edu.cn
Applied Optics
|August 22, 2008
Summary
This study introduces an advanced scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs). The improved method uses a neural network (NN) and a variable step size normalized least-mean square (NLMS) algorithm for precise noise reduction.
Area of Science:
- Infrared imaging technology
- Signal processing algorithms
- Machine learning applications
Background:
- Infrared focal plane arrays (IRFPAs) suffer from fixed pattern noise (FPN) due to detector nonuniformity.
- Existing nonuniformity correction (NUC) methods may lack efficiency and precision in dynamic scenarios.
- Accurate parameter estimation is crucial for effective FPN reduction.
Purpose of the Study:
- To develop an improved scene-adaptive NUC algorithm for IRFPAs.
- To enhance the accuracy and efficiency of FPN elimination.
- To leverage neural networks and advanced adaptive filtering for IRFPAs.
Main Methods:
- A neural network (NN) approach is employed for simultaneous parameter estimation and FPN elimination.
- The traditional LMS algorithm is replaced with a variable step size (VSS) normalized least-mean square (NLMS) adaptive filtering algorithm.
- A novel NN structure is designed for improved target value estimation.
Main Results:
- The VSS-NLMS algorithm demonstrates faster convergence, reduced misadjustment, and lower computational cost compared to LMS.
- The new NN structure significantly enhances calibration precision.
- The proposed NUC method achieves high correction performance, validated by experimental results.
Conclusions:
- The developed scene-adaptive NUC algorithm effectively corrects nonuniformity in IRFPAs.
- The integration of VSS-NLMS and a novel NN structure offers a superior approach to FPN reduction.
- The method shows significant potential for improving infrared image quality.
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