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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(6), pp.11-19
  • DOI : 10.9708/jksci.2019.24.06.011
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 23, 2019
  • Accepted : June 10, 2019
  • Published : June 28, 2019

Yonghee Hong 1 Nam-Hun Song 1 Daehyeon Kim 1 Chan-Won Jun 2 지호진 1

1LIG넥스원
2국방과학연구소

Accredited

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.

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