• Title/Summary/Keyword: Low Illumination

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A Comprehensive and Practical Image Enhancement Method

  • Wu, Fanglong;Liu, Cuiyin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.5112-5129
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    • 2019
  • Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

Performance Evaluations of the Interpolation Methods Under the various illumination intensities and its Application to the Adaptive Interpolation for Image Sensors (이미지센서를 위한 조도에 따른 보간 기법의 성능 평가와 이를 이용한 가변적 보간 기법)

  • Kim, Byung-Su;Paik, Doo-Won
    • Journal of Internet Computing and Services
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    • v.9 no.1
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    • pp.171-177
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    • 2008
  • In this paper we compared the performance of interpolation algorithms for Bayer patterned image sensors under the various illumination intensities. As the interpolation algorithms, we used bilinear color interpolation and adaptive fuzzy color interpolation and our experimentation shows that performance of interpolation algorithms depend on the lighting conditions; in low intensity of illumination, bilinear color interpolation with median filter performs best, in high intensity of illumination, adaptive fuzzy color interpolation performs best, and in between bilinear color interpolation performs best. This study suggested an interpolation scheme which applies different interpolation algorithm according to the intensity of the input image, resuting in the better image quality.

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Analytical Model of Double Gate MOSFET for High Sensitivity Low Power Photosensor

  • Gautam, Rajni;Saxena, Manoj;Gupta, R.S.;Gupta, Mridula
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.13 no.5
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    • pp.500-510
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    • 2013
  • In this paper, a high-sensitivity low power photodetector using double gate (DG) MOSFET is proposed for the first time using change in subthreshold current under illumination as the sensitivity parameter. An analytical model for optically controlled double gate (DG) MOSFET under illumination is developed to demonstrate that it can be used as high sensitivity photodetector and simulation results are used to validate the analytical results. Sensitivity of the device is compared with conventional bulk MOSFET and results show that DG MOSFET has higher sensitivity over bulk MOSFET due to much lower dark current obtained in DG MOSFET because of its effective gate control. Impact of the silicon film thickness and gate stack engineering is also studied on sensitivity.

Illumination Variations in Near-Equatorial Orbit Imaging: A Case Study with Simulated Data of RAZAKSAT

  • Hassan, Aida-Hayati-Mohd;Hashim, Mazlan;Arshad, Ahmad-Sabirin
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.1052-1054
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    • 2003
  • RAZAKSAT is a second micro-satellite mission by Malaysian Satellite Program and is expected for launch in June 2004. Designed to orbit the earth at low-equatorial orbit, RAZAKSAT will meet Malaysia’s immediate needs to rapid data acquisition (real time and more repetitions) to address many operational issues of remote sensing applications, which require availability of current data sets. RAZAKSAT will be among the first remote sensing satellite to orbit the earth at low inclination along the equator, 9$^{\circ}$ with 685km altitude, hence, allows optimal geographical information and environment change within equatorial region be observed with a unique revisit characteristics. The satellite primary payload is MAC, a push-broom type camera with 2.5m of ground sampling distance (GSD) in panchromatic band and 5m of GSD in four multi-spectral bands. This paper describes on the variation of illumination anticipated from simulated RAZAKSAT image, examine its implication to its ground leaving radiances for major applications.

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An Efficient Binarization Method for Vehicle License Plate Character Recognition

  • Yang, Xue-Ya;Kim, Kyung-Lok;Hwang, Byung-Kon
    • Journal of Korea Multimedia Society
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    • v.11 no.12
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    • pp.1649-1657
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    • 2008
  • In this paper, to overcome the failure of binarization for the characters suffered from low contrast and non-uniform illumination in license plate character recognition system, we improved the binarization method by combining local thresholding with global thresholding and edge detection. Firstly, apply the local thresholding method to locate the characters in the license plate image and then get the threshold value for the character based on edge detector. This method solves the problem of local low contrast and non-uniform illumination. Finally, back-propagation Neural Network is selected as a powerful tool to perform the recognition process. The results of the experiments i1lustrate that the proposed binarization method works well and the selected classifier saves the processing time. Besides, the character recognition system performed better recognition accuracy 95.7%, and the recognition speed is controlled within 0.3 seconds.

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Illumination Estimation Based on Nonnegative Matrix Factorization with Dominant Chromaticity Analysis (주색도 분석을 적용한 비음수 행렬 분해 기반의 광원 추정)

  • Lee, Ji-Heon;Kim, Dae-Chul;Ha, Yeong-Ho
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.8
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    • pp.89-96
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    • 2015
  • Human visual system has chromatic adaptation to determine the color of an object regardless of illumination, whereas digital camera records illumination and reflectance together, giving the color appearance of the scene varied under different illumination. NMFsc(nonnegative matrix factorization with sparseness constraint) was recently introduced to estimate original object color by using sparseness constraint. In NMFsc, low sparseness constraint is used to estimate illumination and high sparseness constraint is used to estimate reflectance. However, NMFsc has an illumination estimation error for images with large uniform area, which is considered as dominant chromaticity. To overcome the defects of NMFsc, illumination estimation via nonnegative matrix factorization with dominant chromaticity image is proposed. First, image is converted to chromaticity color space and analyzed by chromaticity histogram. Chromaticity histogram segments the original image into similar chromaticity images. A segmented region with the lowest standard deviation is determined as dominant chromaticity region. Next, dominant chromaticity is removed in the original image. Then, illumination estimation using nonnegative matrix factorization is performed on the image without dominant chromaticity. To evaluate the proposed method, experimental results are analyzed by average angular error in the real world dataset and it has shown that the proposed method with 5.5 average angular error achieve better illuminant estimation over the previous method with 5.7 average angular error.

15×15 Kernel Block Adaptive Median Filter based on LED Illumination Detection Algorithm for Low Rate CamCom (15×15 Kernel Block Adaptive Median Filter를 적용한 저속 카메라 통신용 LED 조명 검출 알고리즘 연구)

  • Han, Jungdo;Lee, Minwoo;Cha, Jae Sang
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.5
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    • pp.143-150
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    • 2018
  • With the rapid development of RF based high speed wireless communication technology, devices that can be applied to IoT networks based on RF bandwidth are rapidly spreading, nevertheless, the development speed of the RF communication is not possible to keep up with the spread of the RF band for wireless communication. In this situation, OWC technology that uses visible light source as a transmitter is attracting attention as a technology that can overcome the band exhaustion problem of RF based wireless communication technology. Although, due to the distortion of the LED illumination shape by camera exposure time and LED blinking period, the LED illumination detection rate is degraded and the RoI setting is inaccurate. In this paper, we propose an adaptive median filter applied LED illumination detection algorithm for low rate CamCom, it is possible to detect a clear RoI and LED illumination. This research will be able to play a role as a complementary material of RF based wireless communication technology efficiently.

Generating a Retinex-based Reflectance Image from a Low-Light Image Using Deep Neural Network (심층 신경망을 이용한 저조도 영상에서 Retinex 기반 반사 영상 생성)

  • Kim, Wonhoi;Hwang, In-Chul;Kim, Manbae
    • Journal of Broadcast Engineering
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    • v.24 no.1
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    • pp.87-96
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    • 2019
  • Improvement of low-light image mainly focuses on the contrast enhancement. Many researches have been carried out for brightness enhancement, contrast improvement and illumination reduction. Recently, the aforementioned approaches have been replaced by artificial neural networks. This paper proposes a methodology that can replace the Retinex-based reflectance image acquisition by deep neural network. Experiments carried out on 102 low-light images validated the feasibility of the replacement by producing PSNR=30.8682(db) and SSIM=0.4345.

Meter Numeric Character Recognition Using Illumination Normalization and Hybrid Classifier (조명 정규화 및 하이브리드 분류기를 이용한 계량기 숫자 인식)

  • Oh, Hangul;Cho, Seongwon;Chung, Sun-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.1
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    • pp.71-77
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    • 2014
  • In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

An Illumination Invariant Traffic Sign Recognition in the Driving Environment for Intelligence Vehicles (지능형 자동차를 위한 조명 변화에 강인한 도로표지판 검출 및 인식)

  • Lee, Taewoo;Lim, Kwangyong;Bae, Guntae;Byun, Hyeran;Choi, Yeongwoo
    • Journal of KIISE
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    • v.42 no.2
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    • pp.203-212
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    • 2015
  • This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.