• Journal of the Optical Society of Korea
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• v.17 no.3
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• pp.255-261
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• 2013

Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.15-23
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• 2017

In this paper, the block reweighting and global offset initialization methods are proposed to complement the improved IRLMS algorithm which is the effective algorithm in registration based SBNUC algorithm. Proposed block weighting method reweights the error map whose abnormal data are excluded. The global offset initialization method compensates the global nonuniformity initially. The ordinary registration based SBNUC algorithm is hard to compensate global nonuniformity because of low scene motion. We employ the proposed methods to improved IRLMS algorithm, and apply it to real-world infrared raw image stream. The result shows that new implementation provides 3.5~4.0dB higher PSNR and convergence speed 1.5 faster then the improved IRLMS algorithm.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.11-19
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• 2019

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.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.429-430
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• 2007

FPA (Focal Plane Arrary)를 이용한 적외선 영상 획득 시스템에서 발생하는 주요 잡음 중 하나는 영상에 존재하는 공간적 고정 패턴 잡음(SFPN, Spatial Fixed Pattern Noise)이다. 이것이 발생하는 주된 요인은 배열을 이루고 있는 각 검출기들과, FPA 출력단에 있는 증폭기의 입출력 응답이 균일하지 않고, 시간이 흐름에 따라 그 응답특성이 변화하기 때문이다. 이 문제를 극복하기 위하여 일반적으로 교정기반 불균일 보정 방법(CBNUC, Calibration Based Nonuniformity Correction)과 장면기반 불균일 보정방법(SBNUC, Scene Based Nonuniformity Correction)이 사용된다. 본 논문은 CBNUC를 사용하는 시스템의 FPA 출력단 회로에 구성된 복수의 증폭기에 존재하는 이득의 차이 및 잡음에 의한 불균일을 보정하기 위한 보간 기법을 제안한다. 실험을 통하여 제안한 기법이 CBNUC 기반 적외선 영상 시스템에서 발생하는 규칙적인 패턴의 SFPN을 효율적으로 제거하는 것을 확인하였다. 또한, 제안한 기법은 CBNUC 기반 적외선 영상 시스템에서 주기적으로 수행해야하는 단일점보정 (OPC, One Point Correction)의 수행횟수를 줄이고, 연산량도 적어 실시간 시스템 구현이 가능하다.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.73-76
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• 2000

A Scene Based Technique(SBT) that corrects linear array infrared detector's nonuniformity is proposed. Basically, this technique dispenses with using temperature references on a linear array infrared detector. To correct the nonuniformity of infrared images, we use three methods. Firstly, we detect bad channels by using the information which is cumulated all the same line pixels. Secondly, a variable window method is applied to compensate more accurately. Thirdly, an adaptive method which updates gain and offset coefficient is used only on a stationary region. These results are demonstrated on a computer simulation with various images. As a result, the nonuniformity is corrected completely, so that images are enhanced and PSNR(peak signal to noise ratio) is improved much.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.81-90
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• 2018

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.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.146-158
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• 2022

The detector of a focal-plane-array mid-wave infrared (MWIR) camera has different response characteristics for each detector pixel, resulting in nonuniformity between detector pixels. In addition, image nonuniformity occurs due to heat generation inside the camera during operation. To solve this problem, in the process of camera manufacturing it is common to use a gain-and-offset table generated from a blackbody to correct the difference between detector pixels. One method of correcting nonuniformity due to internal heat generation during the operation of the camera generates a new offset value based on input frame images. This paper proposes a technique for dividing an input image into block image patches and generating offset values using only homogeneous patches, to correct the nonuniformity that occurs during camera operation. The proposed technique may not only generate a nonuniformity-correction offset that can prevent motion marks due to camera-gaze movement of the acquired image, but may also improve nonuniformity-correction performance with a small number of input images. Experimental results show that distortion such as flow marks does not occur, and good correction performance can be confirmed even with half the number of input images or fewer, compared to the traditional method.