• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.203-207
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• 2018

This paper deals with the design and development of a lens system capable of imaging an infrared image of $3{\sim}5{\mu}m$ wavelength bands with a focal length of 24mm and good atmospheric transmission characteristics. The design used CodeV, a commercial design program, and the optimization is carried out with weighting to eliminate chromatic aberration, spherical aberration and distortion. The designed lens system consists of two lenses consisting of Si and Ge. Each lens has an aspherical surface on one side. And this optical system has the resolution of the characteristics that the MTF value is 0.40 at the line width of 29lp/mm and the MTF value is 0.25 at the line width of 20lp/mm. This optical system is considered to have the capability to be applied to the thermal imaging camera for MWIR using the $206{\times}156$ array infrared detector of $25{\mu}m$ pixels and the $320{\times}240$ array infrared detector of $17{\mu}m$ pixels.

• Journal of Broadcast Engineering
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• v.16 no.5
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• pp.873-882
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• 2011

In this paper, we propose an efficient deinterlacing algorithm which interpolates the missing scan lines by weighted summing of the intra and the inter interpolation pixels according to the spatio-temporal variation. In the spatial interpolation, we adopt a new edge based spatial interpolation method which includes edge directional refinement. The conventional edge dependent interpolation algorithms are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method first finds the edge directions around the pixel to be interpolated and then refines edge direction of the pixel using weighted maximun frequent filter. Futhermore, we improve the accuracy of motion detection by reducing the possibility of motion detection error using 3 tab median filter. In the final interpolation step, we adopt weighted sum of intra and inter interpolation pixels according to spatio-temporal variation ratio, thereby improving the quality in slow moving area. Simulation results show the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Korean Journal of Optics and Photonics
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• v.12 no.1
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• pp.61-66
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• 2001

Defocused Fourier plane holograms are stored in disk-type holographic memories where thin recording media are used, the areal storage density per hologram and the intensity uniformity of the signal beam at the recording plane are studied. As the pixel pitch of the spatial light modulator that represents binary data increases, the storage density per hologram increases if exact Fourier holograms are stored. When defocused Fourier plane holograms are stored, however, we show that there exists an optimal pixel pitch that maximizes the area storage density per hologram in general, to increase the areal storage density per hologram, f/# of the Fourier transform lens that focuses the data image should be as small as possible. In this case, not only the intensity distribution at the recording plane but also the recording area becomes very sensitive to the degree of defocusing. Therefore, even if the exact Fourier plane holograms are stored, the defocusing effect owing to the medium thickness should be taken into account to achieve the maximal areal storage density per hologram.logram.

• Aerospace Engineering and Technology
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• v.8 no.2
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• pp.75-82
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• 2009

In order to help general users to analyze the KOMPSAT-2 data, an application of sensor modeling to commercial software was explained in this document. The sensor modeling is a basic step to extract the quantity and quality information from KOMPSAT-2 data. First, we introduced the contents and type of ancillary data offered with KOMPSAT-2 PAN image data, and explained how to use it with commercial software. And then, we applied the polynomial-base and refine RFM sensor modeling with ground control points. In the polynomial-base sensor modeling, the accuracy which is average RMSE of check points is highest when the satellite position was calculated by type of 1st order function and the satellite attitude was calculated by type of 1st order function for (Y axis), (Z axis) or constant for (X axis), (Y axis), (Z axis) in perspective center position and satellite attitude parameters. As a result of refine RFM sensor modeling, the accuracy is less than 1 pixel when we applied affine model..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1867-1873
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• 2015

Edge detection is one of image processing techniques applied for a variety of purposes in a number of areas and it is used as a necessary pretreatment process in most applications. In the conventional edge detection methods, there are Sobel, Prewitt, Roberts and LoG, etc using a fixed weights mask. Since conventional edge detection methods apply the images to the fixed weights mask, the edge detection characteristics appear somewhat insufficient. Therefore in this study, an algorithm for detecting the edge is proposed by applying the cross mask based on the center pixel and up, down, left and right mask based on the surrounding pixels of center pixel in order to solve these problems. And in order to assess the performance of proposed algorithm, it was compared with a conventional Sobel, Roberts, Prewitt and LoG edge detection methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1702-1708
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• 2013

OLED has good efficiency of power consumption by having no power consumption from black color as different with LCD. when it has white color, all RGB pixel should be glowing with high power consumption and that can make it has short life time. This paper suggest the way of low power consumption for OLED panel using adaptive luminance enhancement with color compensation and implement it as hardware. This way which is based on luminance information of input image makes converted luminance value from each pixel in real time. There is with using the basic idea of chromaticity reduction algorithm, showing new algorithm of color correction. And performance of proposed method was confirmed by comparing the conventional method in experiments about 48.43% current reduction. The proposed method was designed by Verilog HDL and was verified by using OpenCV and Windows Program.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.371-379
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• 2009

In this paper, we proposed new de-interlacing algorithm for video data using intensity gradient filter and median filter with texture detection in the image block. We first introduce the texture detection. According to texture detection, the current region is determined into smooth region or texture region. In case that the smooth region interpolated by median filter. In addition, in case of the texture region, we calculate missing pixel value using intensity gradient filter. Therefore, we analyze the local region feature using the texture detection and classify each missing pixel into two categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results show that the proposed algorithm performs well with a variety of moving sequences compared with conventional intra-field method in the literature.

• Journal of radiological science and technology
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• v.29 no.4
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• pp.237-240
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• 2006

The Noise of CR Systems is made up of X-ray quantum mottle and additional Imaging plate's structure noise, photon noise of lumine cence, noise of electrometer, quantization noise etc. In this Regius 150 system, SNR was increased from 8.2 to 30 with linearily according to radiation dose from 0.1 mR to 100 mR. It means that the Regius 150 system has enough trustability because of SNR is over 5 by Rose Model. NPS was calculated two dementional Fourier Transform with shake of pixel value in the white Image. In the spatial frequence range of $0.5\;lp/mm{\sim}2.5\;lp/mm$, the NPS was distributed $10^{-4}{\sim}10^{-5}$ at 1 mR X-ray dose. That is similar result compare other systems to that of Kodak CR system reported by Carlu, HR-CR system reported by Dobbins.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.236-241
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• 1998

The NOAA AVHRR remote sense SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the seas adjacent to Korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple 557 images, all of images must be aligned exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which yields automatic detections of cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$ 3$^{\circ}C$ as a criterion of SST anomalies). The remote sense SST data are tuned by comparing remote sense data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel. The SST anomalies are studied by statistical method. We found that the SST anomalies are rather persistent with time scales between 1 and 2 months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST Model fit of SST anomalies to the Markov process model yields that autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. We plan to improve our algorithms of automatic cloud pixel detection and prediction of future SST. Our algorithm is expected to be incorporated to the operational real time service of SST around Korea.