Abstract
The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed pattern noise. In this study, an improved neural networks based method for nonuniformity correction (NUC) is presented. In the improved method, the correction process of neural networks is decomposed from one step to two steps to fine the correction results. Besides, it uses a local median value of each neuron’s output as the desired output for each neuron. Experimental results show that the improved algorithm can eliminate fixed stripe noise and stochastic noise in raw images and make infrared images more slippery.
Keywords
- infrared focal plane array
- nonuniformity correction
- neural networks
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© 2012 Springer-Verlag Berlin Heidelberg
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Tan, D., Zhang, A. (2012). Improved Neural Networks Based Method for Infrared Focal Plane Arrays Nonuniformity Correction. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_13
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DOI: https://doi.org/10.1007/978-3-642-33478-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
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