Scene-based Nonuniformity Correction for Neural Network Complemented by Reducing Lense Vignetting Effect and Adaptive Learning rate (비네팅효과 감소와 가변학습률로 보완한 신경망 구조의 불균일 보정)

Gun-hyo No (노건효); Yonghee Hong (홍용희); Jin-ho Park (박진호); 지호진

doi:10.9708/jksci.2018.23.07.081

Scene-based Nonuniformity Correction for Neural Network Complemented by Reducing Lense Vignetting Effect and Adaptive Learning rate

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(7), pp.81-90
DOI : 10.9708/jksci.2018.23.07.081
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : April 9, 2018
Accepted : June 20, 2018
Published : July 31, 2018

Gun-hyo No ¹, Yonghee Hong ¹, Jin-ho Park ¹, 지호진 ¹

¹LIG넥스원

Accredited

ABSTRACT

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner’s neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner’s neural network method(NNM) and Torres’s reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm

KEYWORDS

SBNUC, improved NNM, Neural Network, statistic, scene based nonuniformity correction

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Scene-based Nonuniformity Correction for Neural Network Complemented by Reducing Lense Vignetting Effect and Adaptive Learning rate

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Scene-based Nonuniformity Correction for Neural Network Complemented by Reducing Lense Vignetting Effect and Adaptive Learning rate

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.