• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.67-71
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• 2016

This paper addresses a problem of defocus map recovery from single image. We describe a simple effective approach to estimate the spatial value of defocus blur at the edge location of the image. At first, we perform a re-blurring process using Gaussian function with input image, and calculate a gradient magnitude ratio with blurring amount between input image and re-blurred image. Then we get a full defocus map by propagating the blur amount at the edge location. Experimental result reveals that our method outperforms a reliable estimation of depth map, and shows that our algorithm is robust to noise, inaccurate edge location and interferences of neighboring edges within input image.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.858-869
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• 2023

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

• 한국가시화정보학회:학술대회논문집
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• 2005.12a
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• pp.6-10
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• 2005

PIV(Particle image velocimetry) presents the flow velocity of whole flow fields in a fraction of a second. Conventional PIV method uses two optical axis configuration during the image grabbing process. That is, the illumination plane and the recording plane must be parallel. This configuration is very natural to grab the whole field without the image distortion. In the real problem, it is often to meet the situation which this configuration is hard to be fulfilled. In this study, we developed new PIV method which only uses single optical axis to grab the particle images. This new PIV method become possible by utilizing the scanning method similar to echo PIV technique. One particle image of scanning PIV consists of scanned several line images and by repeating this scanning process, two particle images were grabbed and processed to produce the velocity vectors.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.247-249
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• 2007

Crown parameters are important roles in tree species identification, because the canopy is the aggregate of all the crowns. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify and classify single tree species. In this paper, shape parameters derived from pixel-based crown area measurements and texture features derived from GLCM parameters in QuickBird image were tested and compared for individual tree species identification. As expected, initial studies have shown that the crown parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees within the compartment(less than forest stand) for single extraction from spaceborne high resolution image.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.2 s.257
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• pp.181-187
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• 2007

Conventional PIV method uses two optical axis configuration during the image grabbing process. That is, the illumination plane and the recording plane must be parallel. This configuration is very natural to grab the whole field without the image distortion. In the real problem, it is often to meet the situation when this configuration is hard to be fulfilled. In the present study, the new PIV method which uses only single optical axis to grab the particle images is developed. This new PIV method becomes possible by utilizing the scanning method similar to the echo PIV technique. One particle image of the scanning PIV consists of scanned several line images and by repeating this scanning process, two particle images were grabbed and processed to produce the velocity vectors. An optimization study was performed to find parameters which minimize the measurement errors. The effects of particle diameter, beam overlap ratio and particle number density were investigated.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.98-101
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• 2019

At present, deep convolutional neural networks have made a very important contribution in single-image super-resolution. Through the learning of the neural networks, the features of input images are transformed and combined to establish a nonlinear mapping of low-resolution images to high-resolution images. Some previous methods are difficult to train and take up a lot of memory. In this paper, we proposed a simple and compact deep recursive residual network learning the features for single image super resolution. Global residual learning and local residual learning are used to reduce the problems of training deep neural networks. And the recursive structure controls the number of parameters to save memory. Experimental results show that the proposed method improved image qualities that occur in previous methods.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.246-251
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• 2014

In this paper, a high-speed CMOS Image Sensor (CIS) based on a 10-bit two step Single Slope A/D Converter (SS-ADC) is proposed. The A/D converter is composed of both 5-bit coarse ADC and a 6-bit fine ADC, and the conversion speed is 10 times faster than that of the single-slope A/D convertor. In order to reduce the pixel noise, further, a Hybrid Correlated Double Sampling (H-CDS) is also discussed. The proposed A/D converter has been fabricated with 0.13um 1-poly 4-metal CIS process, and it has a QVGA ($320{\times}240$) resolution. The fabricated chip size is $5mm{\times}3mm$, and the power consumption is about 35 mW at 3.3 V supply voltage. The measured conversion speed is 10 us, and the frame rate is 220 frames/s.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.8
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• pp.1376-1386
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• 1997

The purpose of this study is to investigate how one's visual evaluations are affected by variations in internal designs of tailored jackets. Major findings are: 1. Of the 35 pairs of adjectives, the factor analysis singled out the following five major factors (total variance of 55.62%): elegance, maturity, neatness, hard/softness, and evaluation. 2. The image of tailored jackets were found to be significantly affected by the length of its collar: jackets with long collar were perceived elegant, graceful, neat, mature and slim. 3. The number of buttons was also found to have significant impact on the image of tailored jackets: A one-button jacket, single or double, was perceived graceful, mature, formal, neat, natural, brisk, slim and so on. Its image deteriorated as the number of buttons increased. 4. Single jackets were found to be significantly different from Double jacket in their images. The former were perceived elegant, neat, and natural.

• Journal of the Optical Society of Korea
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• v.15 no.2
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• pp.140-145
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• 2011

Joint transform correlator (JTC) is a well known tool for color pattern recognition for a color image. Color images have red, green and blue components, thus in conventional JTC, three input channels of these color components are necessary for color pattern recognition. This paper proposes a new technique of color pattern recognition by decomposing the color image into three color components and recombining those components into a single gray image in the input plane. This new technique needs single input channel and single output CCD camera, thus a simple JTC can be used. We present various kinds of simulated results to show that our newly proposed technique can accurately recognize and discriminate color differences.