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

다국어 초록 (Multilingual 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.

목차 (Table of Contents)

• Abstract
• I. Introduction
• II. Preliminaries
• III. improved NNM SBNUC
• IV. The Proposed Scheme
• V. Conclusions
• REFERENCES

참고문헌 (Reference)

1 Chao Zuo, "Scene-based nonuniformity correction method using multiscale constant statistics" 2011

2 Yang Chunling, "Researches on New Non-uniformity Correction Algorithm for Resistor Array" 235-238, 2010

3 Kobi Cohen, "On Projected Stochastic Gradient Descent Algorithm with Weighted Averaging for Least Squares Regression" Institute of Electrical and Electronics Engineers (IEEE) 62 (62): 5974-5981, 2017

4 Abraham Friedenberg, "Nonuniformity two-point linear correction errors in infrared focal plane arrays" SPIE-Intl Soc Optical Eng 37 (37): 1251-, 1998

5 J. Harris., "Minimizing the 'ghosting' artifact in scene-based nonuniformity correction" 3377 : 106-113, 1998

6 Shupeng Wang, "Kalman Filter for Stripe Non-uniformity Correction in Infrared Focal Plane Arrays" 124-127, 2016

7 D.A. Scribner, "Infrared focal plane array technology" Institute of Electrical and Electronics Engineers (IEEE) 79 (79): 66-85, 1991

8 Lee Jongho, "Improvement on a optimi zation algorithm for non-uniformity correction of infrared videos" KAIST, Department of Electrical Engineering 2013

9 Chao Zuo, "Improved interframe registration based nonuniformity correction for focal plane arrays" Elsevier BV 55 (55): 263-269, 2012

10 Chao Zuo, "Improved interframe registration based nonuniformity correction for focal plane arrays" Elsevier BV 55 (55): 263-269, 2012

11 Rong Sheng-Hui, "Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array" Elsevier BV 76 : 691-697, 2016

12 Nicolas Celedon, "FPGA-based Neural Network for Nonuniformity Correction on Infrared Focal Plane Arrays" 193-200, 2012

13 In-Seob Song, "Digital implementation of scene-based non-uniformity correction for microscan-mode infrared cameras" Institution of Engineering and Technology (IET) 35 (35): 1068-, 1999

14 Meng Sheng, "Calibration-based NUC Method in Real time Based on IRFPA" 22 : 372-380, 2011

15 D.A.Scribner, "Adaptive retina-like preprocessing for imaging detector arrays" 1955-1960, 1993

16 Sergio N. Torres, "Adaptive Scene-Based Non-Uniformity Correction Method for Infrared-Focal Plane Arrays" 2003

17 Sergio N. Torres, "A Neural Network for Nonuniformity and Ghosting Correction of Infrared Image Sequences" 1208-1216, 2005