26 October 2013 Adaptive bad pixel correction algorithm for IRFPA based on PCNN
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Proceedings Volume 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis; 891705 (2013) https://doi.org/10.1117/12.2032065
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Bad pixels and response non-uniformity are the primary obstacles when IRFPA is used in different thermal imaging systems. The bad pixels of IRFPA include fixed bad pixels and random bad pixels. The former is caused by material or manufacture defect and their positions are always fixed, the latter is caused by temperature drift and their positions are always changing. Traditional radiometric calibration-based bad pixel detection and compensation algorithm is only valid to the fixed bad pixels. Scene-based bad pixel correction algorithm is the effective way to eliminate these two kinds of bad pixels. Currently, the most used scene-based bad pixel correction algorithm is based on adaptive median filter (AMF). In this algorithm, bad pixels are regarded as image noise and then be replaced by filtered value. However, missed correction and false correction often happens when AMF is used to handle complex infrared scenes. To solve this problem, a new adaptive bad pixel correction algorithm based on pulse coupled neural networks (PCNN) is proposed. Potential bad pixels are detected by PCNN in the first step, then image sequences are used periodically to confirm the real bad pixels and exclude the false one, finally bad pixels are replaced by the filtered result. With the real infrared images obtained from a camera, the experiment results show the effectiveness of the proposed algorithm.
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Hanbing Leng, Zuofeng Zhou, Jianzhong Cao, Bo Yi, Aqi Yan, Jian Zhang, "Adaptive bad pixel correction algorithm for IRFPA based on PCNN", Proc. SPIE 8917, MIPPR 2013: Multispectral Image Acquisition, Processing, and Analysis, 891705 (26 October 2013); doi: 10.1117/12.2032065; https://doi.org/10.1117/12.2032065
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