• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.295-304
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• 2006

Image fusion is defined as making new image by merging two or more images using special algorithms. In case of remote sensing, it means fusing multispectral low-resolution remotely sensed image with panchromatic high-resolution image. Generally, hyperspectral image fusion is accomplished by utilizing fusion technique of multispectral imagery or spectral unmixing model. But, the former may distort spectral information and the latter needs endmember data or additional data, and has a problem with not preserving spatial information well. This study proposes a new algorithm based on two stage spectral unmixing model for preserving hyperspectral image's spectral information. The proposed fusion technique is implemented and tested using Hyperion and ALI images. it is shown to work well on maintaining more spatial/spectral information than the PCA/GS fusion algorithms.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.403.1-403.1
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• 2014

In conventional far-field microscopy, two objects separated closer than approximately half of an emission wavelength cannot be resolved, because of the fundamental limitation known as Abbe's diffraction limit. During the last decade, several super-resolution methods have been developed to overcome the diffraction limit in optical imaging. Among them, super-resolution optical fluctuation imaging (SOFI) developed by Dertinger et al [1], employs the statistical analysis of temporal fluorescence fluctuations induced by blinking phenomena in fluorophores. SOFI is a simple and versatile method for super-resolution imaging. However, due to the uncontrollable blinking of fluorophores, there are some limitations to using SOFI for several applications, including the limitations of available blinking fluorophores for SOFI, a requirement of using a high-speed camera, and a low signal-to-noise ratio. To solve these limitations, we present a new approach combining SOFI with speckle pattern illumination to create illumination-induced optical fluctuation instead of blinking fluctuation of fluorophore.. This technique effectively overcome the limitations of the conventional SOFI since illumination-induced optical fluctuation is possible to control unlike blinking phenomena of fluorophore. And we present the sub-diffraction resolution image using SOFI with speckle illumination.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4127-4133
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• 2024

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.5
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• pp.580-585
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• 1988

In this paper, a low frequency acoustic microscope system has been built for the purpose of detecting subsurface defects in materials. The lateral resolution of acoustic microscope is studied and analyzed in order to evaluate that system performance. And a NDE technique is demonstrated by using this system. In the focused and defocused mode of operation, the acoustic microscope system showed in experiment that its lateral resolution was about 0.5 mm at a frequency of operation of 3MHz on a fused-quartz sample with seeded circular cracks ranged in size from 0.2 mm to 1.0 mm in diameter wihtin 1 mm of the surface. It also showed the acoustic image of 100 won coin with fine contrast.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.239.2-239.2
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• 2015

Graphene is a most interesting material due to its unique and outstanding properties. However, semi-metallic properties of graphene along with zero bandgap energy structure limit further application to optoelectronic devices. Recently, many researchers have shown that band gap can be induced in the Bernal stacked bilayer graphene. Several methods have been used for the controlled growth of the Bernal staked bilayer graphene, but it is still challenging to control the growth process. In this paper, we synthesize the large area Bernal stacked bilayer graphene using multi heating zone low pressure chemical vapor deposition (LPCVD). The synthesized bilayer graphenes are characterized by Raman spectroscopy, optical microscope (OM), scanning electron microscopy (SEM). High resolution transmission electron microscopy (HRTEM) is used for the observation of atomic resolution image of the graphene layers.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.180-187
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• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.

• Journal of radiological science and technology
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• v.41 no.2
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• pp.115-122
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• 2018

We aims to perform comparative analysis on the dose area and image qualities varying on the slice thickness when using Automatic Exposure Controller (AEC) and manual exposure; thus, it wants to suggest a measure to reduce exposure dose by setting the optimal examination condition for each slice thickness. The method was to set the thickness as Thin, Normal, and Heavy adult and evaluate the dose area, spatial resolution, low contrast resolution, Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) according to each slice thickness by using the AEC and the manual exposure controller. The dose area according to each slice thickness all increased both when using the AEC and the manual exposure. However, the manual exposure showed lower dose area product than the AEC. Spatial resolutions and low contrast resolutions were all observed to be higher than the evaluation standard. Also, the SNR and CNR of each thickness all increased when using the AEC. When using the manual exposure, SNR and CNR increased in all cases other than the Heavy Adult. Consequently, the Thin and Normal Adult showed dose reduction about 2 times when using the manual exposure controller, while ensuring the image quality. Heavy adult was able to maintain good image quality by using AEC.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.385-389
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• 2019

Knowledge of abundance, or population size, is fundamental in wildlife conservation and management. Camera-trapping, in combination with capture-recapture methods, has been extensively applied to estimate abundance and density of individually identifiable animals due to the advantages of being non-invasive, effective to survey wide-ranging, elusive, or nocturnal species, operating in inhospitable environment, and taking low labor. We assessed the possibility of using coat patterns from images to identify an individual leopard cat (Prionailurus bengalensis), a Class II endangered species in South Korea. We analyzed leopard cat images taken from Digital Single-Lense Relfex camera (high resolution, 18Mpxl) and camera traps (low resolution, 3.1Mpxl) using HotSpotter, an image matching algorithm. HotSpotter accurately top-ranked an image of the same individual leopard cat with the reference leopard cat image 100% by matching facial and ventral parts. This confirms that facial and ventral fur patterns of the Amur leopard cat are good matching points to be used reliably to identify an individual. We anticipate that the study results will be useful to researchers interested in studying behavior or population parameter estimates of Amur leopard cats based on capture-recapture models.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.