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http://dx.doi.org/10.9708/jksci.2019.24.06.011

Scene-based Nonuniformity Correction by Deep Neural Network with Image Roughness-like and Spatial Noise Cost Functions  

Hong, Yong-hee (LIG Nex1)
Song, Nam-Hun (LIG Nex1)
Kim, Dae-Hyeon (LIG Nex1)
Jun, Chan-Won (Defense Industry Technology Center)
Jhee, Ho-Jin (LIG Nex1)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.6, 2019 , pp. 11-19 More about this Journal
Abstract
In this paper, a new Scene-based Nonuniformity Correction (SBNUC) method is proposed by applying Image Roughness-like and Spatial Noise cost functions on deep neural network structure. The classic approaches for nonuniformity correction require generally plenty of sequential image data sets to acquire accurate image correction offset coefficients. The proposed method, however, is able to estimate offset from only a couple of images powered by the characteristic of deep neural network scheme. The real world SWIR image set is applied to verify the performance of proposed method and the result shows that image quality improvement of PSNR 70.3dB (maximum) is achieved. This is about 8.0dB more than the improved IRLMS algorithm which preliminarily requires precise image registration process on consecutive image frames.
Keywords
Deep Neural Network; Image Roughness-like; SN; SBNUC; Improved IRLMS;
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Times Cited By KSCI : 2  (Citation Analysis)
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