Non-uniformity correction algorithm for IRFPA based on local scene statistics and improved neural network

In this paper a new non-uniformity correction algorithm is proposed which is based on local scene statistics and improved neural network. The new algorithm firstly uses local scene statistics to filter out low frequency noise over infrared image and reduce the ghosting artifacts of a previously deve...

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Bibliographic Details
Published in2012 5th International Congress on Image and Signal Processing pp. 378 - 381
Main Authors Shaosheng Dai, Changchuan Chen, Chuanxi Wu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Algorithm design and analysis
Detectors
Equations
Imaging
IRFPA
local scene statistics
neural network
Neural networks
Noise
non-uniformity correction
Signal processing algorithms
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Summary:In this paper a new non-uniformity correction algorithm is proposed which is based on local scene statistics and improved neural network. The new algorithm firstly uses local scene statistics to filter out low frequency noise over infrared image and reduce the ghosting artifacts of a previously developed scene-based non-uniformity correction method, then uses improved neural network algorithm to correct the nonuniformity of infrared focal plane array (IRFPA). Experiments show that the proposed algorithm can effectively filter out low frequency noise and improve corrected image quality.
ISBN:9781467309653
1467309656
DOI:10.1109/CISP.2012.6469726