• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.215-221
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• 2010

To assess the clinical value of time resolved imaging of contrast kinetics(TRICKS) MRA by comparison with conventional time of flight(TOF) MR angiography. Both TOF-MRA and TRICKS-MRA were performed in 17 patients with cerebrovascular disease and in 6 patients with brain tumor. Among 17 cerebraovascular patients, digital subtraction angiography(DSA) data were also obtained in 11 patients. TOF-MRA showed good spatial resolution but short in temporal resolution. Although TRICKS-MRA showed somewhat low spatial resolution, it showed superior temporal resolution by distinguishing vessel and tumor in all patients. Also, from the analysis of vessel-tumor relationship, TRICKS-MRA showed better performance than TOF-MRA. TRICKS-MRA makes it possible to image arterial, capillary and venous phase sequentially with very speedy manner and therefore, the clinical use of this method is highly suggestive for future use.

• Imaging Science in Dentistry
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• v.37 no.3
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• pp.157-163
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• 2007

Purpose: To evaluate the characteristics of (widely used) cone beam computed tomography (CBCT) images. Materials and Methods: Images were obtained with CT performance phantoms (The American Association of Physicists in Medicine; AAPM). CT phantom as the destination by using PSR $9000N^{TM}$ dental CT system (Asahi Roentgen Ind. Co., Ltd., Japan) and i-CAT CBCT (Imaging Science International Inc., USA) that have different kinds of detectors and field of view, and compared these images with the CT number for linear attenuation, contrast resolution, and spatial resolution. Results: CT number of both PSR $9000N^{TM}$ dental CT system and i-CAT CBCT did not conform to the base value of CT performance phantom. The contrast of i-CAT CBCT is higher than that of PSR $9000N^{TM}$ dental CT system. Both contrasts were increased according to thickness of cross section. Spatial resolution and shapes of reappearance was possible up to 0.6 mm in PSR $9000N^{TM}$ dental CT system and up to 1.0 mm in i-CAT CBCT. Low contrast resolution in region of low contrast sensitivity revealed low level at PSR $9000N^{TM}$ dental CT system and i-CAT CBCT. Conclusion: CBCT images revealed higher spatial resolution, however, contrast resolution in region of low contrast sensitivity was the inferiority of image characteristics.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.409-420
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• 2007

Since 1988, pine wilt disease has spread over rapidly in Korea. It is not easy to detect the damaged pine trees by pine wilt disease from conventional remote sensing skills. Thus, many possibilities were investigated to detect the damaged pines using various kinds of remote sensing data including high spatial resolution satellite image of 2000/2003 IKONOS and 2005 QuickBird, aerial photos, and digital airborne data, too. Time series of B&W aerial photos at the scale of 1:6,000 were used to validate the results. A local maximum filtering was adapted to determine whether the damaged pines could be detected or not at the tree level from high resolution satellite images, and to locate the damaged trees. Several enhancement methods such as NDVI and image transformations were examined to find out the optimal detection method. Considering the mean crown radius of pine trees, local maximum filter with 3 pixels in radius was adapted to detect the damaged trees on IKONOS image. CIR images of 50 cm resolution were taken by PKNU-3(REDLAKE MS4000) sensor. The simulated CIR images with resolutions of 1 m, 2 m, and 4 m were generated to test the possibility of tree detection both in a stereo and a single mode. In conclusion, in order to detect the pine tree damaged by pine wilt disease at a tree level from satellite image, a spatial resolution might be less than 1 m in a single mode and/or 1 m in a stereo mode.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1461-1473
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• 2021

The purpose of this study is to ascertain how the level of resolution of land cover data affects on the local distribution and diffusion of fine dust. the CALPUFF model, which considers the spatio-temporal terrain conditions and changes in weather conditions, was used to estimate PM10 concentration in the Pyeongchon, Anyang-si, Gyeonggi-do. Three different resolutions of land cover data including 20 m, 50 m, and 100 m were compared as the input of the modeling. Using higher resolution land cover data (20 m), the wind speed of the simulated region was the largest and the PM10 concentration was the lowest. Through this study, we confirm that the resolution level of land-use/cover data can affect the local distribution and diffusion of fine dust, which can be detected by CALPUFF. Therefore, when using CALPUFF to simulate fine dust in the future, it can be suggested that checking the impact on spatial resolution according to the form of land cover in advance and proceeding with the simulation can achieve mote accurate results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.685-686
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• 2012

• Journal of radiological science and technology
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• v.40 no.3
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• pp.393-400
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• 2017

The purpose of this study is to suggest a method to reduce the dose by Analyzing the dose area product (DAP) and image quality according to the change of tube current using NEMA Phantom. The spatial resolution and low contrast resolution were used as evaluation criteria in addition to signal to noise ratio (SNR) and contrast to noise ratio (CNR), which are important image quality parameters of intervention. Tube voltage was fixed at 80 kVp and the amount of tube current was changed to 20, 30, 40, and 50 mAs, and the dose area product and image quality were compared and analyzed. As a result, the dose area product increased from $1066mGycm^2$ to $6160mGycm^2$ to 6 times as the condition increased, while the spatial resolution and low contrast resolution were higher than 20 mAs and 30 mAs, Spatial resolution and low contrast resolution were observed below the evaluation criteria. In addition, the SNR and CNR increased up to 30 mAs, slightly increased at 40 mAs, but not significantly different from the previous one, and decreased at 50 mAs. As a result, the exposure dose significantly increased due to overexposure of the test conditions and the image quality deteriorated in all areas of spatial resolution, low contrast resolution, SNR and CNR.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.163-173
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• 2014

When evaluating the spatial resolution images and evaluation of low contrast resolution using CT standard phantom, and might present a automated quantitative evaluation method for minimizing errors by subjective judgment of the evaluator be, and try to evaluate the usefulness. 120kVp and 250mAs, 10mm collimation, SFOV(scan field of view) of 25cm or more than, exposure conditions DFOV(display field of view) of 25cm, and were evaluated the 24 passing images and 20 failing images taken using a standard reconstruction algorithm by using the Nuclear Associates, Inc. AAPM CT Performance Phantom(Model 76-410). Quantitative evaluation of low contrast resolution and spatial resolution was using an evaluation program that was self-developed using the company Mathwork Matlab(Ver. 7.6. (R2008a)) software. In this study, the results were evaluated using the evaluation program that was self-developed in the evaluation of images using CT standard phantom, it was possible to evaluate an objective numerical qualitative evaluation item. First, if the contrast resolution, if EI is 0.50, 0.51, 0.52, 0.53, as a result of evaluating quantitatively the results were evaluated qualitatively match. Second, if CNR is -0.0018~-0.0010, as a result of evaluating quantitatively the results were evaluated qualitatively match. Third, if the spatial resolution, as a result of using a image segmentation technique, and automatically extract the contour boundary of the hole, as a result of evaluating quantitatively the results were evaluated qualitatively match.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1604-1611
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• 2020

Light field image captured by a microlens array-based camera has many limitations in practical use due to its low spatial resolution and angular resolution. High spatial resolution images can be easily acquired with a single image super-resolution technique that has been studied a lot recently. But there is a problem in that high angular resolution images are distorted in the process of using disparity information inherent among images, and thus it is difficult to obtain a high-quality angular resolution image. In this paper, we propose light field angular super-resolution that extracts an initial feature map using an dilated convolutional neural network in order to effectively extract the view difference information inherent among images and generates target image using a residual neural network. The proposed network showed superior performance in PSNR and subjective image quality compared to existing angular super-resolution networks.