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Sensors 2018, 18(1), 211; doi:10.3390/s18010211

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction

School of Optoelectronics, Beijing Institute of Technology, Key Laboratory of Photo-electronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China
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Received: 22 October 2017 / Revised: 10 January 2018 / Accepted: 10 January 2018 / Published: 13 January 2018
(This article belongs to the Section Physical Sensors)
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Abstract

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods. View Full-Text
Keywords: fixed pattern noise; nonuniformity correction; noise estimation; neural network fixed pattern noise; nonuniformity correction; noise estimation; neural network
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Li, Y.; Jin, W.; Zhu, J.; Zhang, X.; Li, S. An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction. Sensors 2018, 18, 211.

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