• Atmosphere
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• v.31 no.5
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.672-684
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• 2020

Restoring a low resolution and motion blurred light field has become essential due to the growing works on parallax-based image processing. These tasks are known as light-field enhancement process. Unfortunately, only a few state-of-the-art methods are introduced to solve the multiple problems jointly. In this work, we design a framework that jointly solves light field spatial super-resolution and motion deblurring tasks. Particularly, we generate a straight-forward neural network that is trained under low-resolution and 6-degree-of-freedom (6-DOF) motion-blurred light field dataset. Furthermore, we propose the strategy of local region optimization on the adversarial network to boost the performance. We evaluate our method through both quantitative and qualitative measurements and exhibit superior performance compared to the state-of-the-art methods.

• Smart Media Journal
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• v.9 no.4
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• pp.134-143
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• 2020

Vehicle License Plate Recognition is one of the approaches for transportation and traffic safety networks, such as traffic control, speed limit enforcement and runaway vehicle tracking. Although it has been studied for decades, it is attracting more and more attention due to the recent development of deep learning and improved performance. Also, it is largely divided into license plate detection and recognition. In this study, experiments were conducted to improve license plate detection performance by utilizing various object detection methods and WPOD-Net(Warped Planar Object Detection Network) model. The accuracy was improved by selecting the method of detecting the vehicle(s) and then detecting the license plate(s) instead of the conventional method of detecting the license plate using the object detection model. In particular, the final performance was improved through the process of removing noise existing in the image by using the Fast-SRGAN model, one of the Super-Resolution methods. As a result, this experiment showed the performance has improved an average of 4.34% from 92.38% to 96.72% compared to previous studies.

• Journal of radiological science and technology
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• v.46 no.1
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• pp.15-21
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• 2023

This study was purpose to assessment of the resolution characteristics by using ATS 535H Basic quality assurance (QA) phantom for ultrasound. The ultrasound equipment was used Logiq P6 (Ultrasound, GE Healthcare System, Chicago, IL, USA). And the ultrasound transducer were used Convex 4C (4~5.5 MHz), Linear 11L (10~13 MHz), Sector 3SP (3~5.5 MHz) probe. As for the noise power spectrum (NPS) comparison results by using ATS 535H Basic QA ultrasound phantom and Convex 4C, Linear 11L, Sector 3SP probe. The NPS value of the Convex 4C probe image was 0.0049, Linear 11L probe image was 0.0049, Sector 3SP probe image was 0.1422 when the frequency is 1.0 mm^-1. The modulation transfer function (MTF) comparison results by using ATS 535H Basic QA ultrasound phantom and Linear 11L probe the MTF value of the 3 cm focus image was 0.7511 and 4 cm focus image was 0.9001 when the frequency is 1.0 mm^-1. This study was presented characteristics of spatial resolution a quantitative evaluation methods by using ultrasound medical images for QA of ultrasound medical QA phantom. The quality control (QC) for equipment maintenance can be efficiently used in the clinic due to the quantitative evaluation of the NPS and MTF as the standard methods. It is meaningful in that it is applied mutatis mutandis and presented the results of physical resolution characteristics of the ultrasound medical image.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.166-169
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• 2004

Change detection is widely used taxation, military fields, etc. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use and automatic method for changed area detection should be developed. In this research, characteristics of Kompsat images are reviewed in perspective of change detection and various change detection method applicable to are tested.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.518-521
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• 2004

Change detection is one of the common research topics in remote sensing. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use. If changed area images are automatically produced, these images will be used in public area such as regional planning, regional development managements. In this research, we developed new change detection method applicable KOMPSAT EOC images. This method automatically produces subset images in changed area.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.110-119
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• 1995

The lateral resolution in an ultrasound imaging system is one of the most important factors for quality of the image and is determined by the beam focusing. For the better lateral resolution SDF(Sampled Delay Focusing) capable of digital focusing was proposed. The second-order sampling, one of band-width sampling methods, is suggested as being the best suitable for SDF because it allows total digital processing and is simple and economical. By proving that it introduces too much error, this article shows the second-order sampling is not appropriate for sampling of the wide-band signal generally used in ultrasound imaging systems. Also, this article suggests new sampling methods that maintain the advantages and reduce the unavoidable errors of the second-order sampling method. From computer simulation it is expected that the proposed methods reduce the errors of the second-order sampling method and can be used in real applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1672-1686
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• 2013

In this paper, we propose a hybrid super-resolution algorithm robust to cut-change. Existing single-frame based super-resolution algorithms are usually fast, but quantity of information for interpolation is limited. Although the existing multi-frame based super-resolution algorithms generally robust to this problem, the performance of algorithm strongly depends on motions of input video. Furthemore at boundary of cut, applying of the algorithm is limited. In the proposed method, we detect a define boundary of cut using cut-detection algorithm. Then we adaptively apply a single-frame based super-resolution method to detected cut. Additionally, we propose algorithms of normalizing motion vector and analyzing pattern of edge to solve various problems of existing super-resolution algorithms. The experimental results show that the proposed algorithm has better performance than other conventional interpolation methods.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Progress in Medical Physics
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• v.33 no.4
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• pp.63-71
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• 2022

Purpose: To clarify the relationship between the diameter of the simulated bead and the Z-resolution of the tomosynthesis image. Methods: A simulated bead was placed on a 1,024×1,024×1,024-pixel base image. The diameters were set to 0.025, 0.05, 0.1, 0.2, 0.3, 0.7, 1.0, and 1.3 mm. A bead was placed at the center of the base image and projected at a simulated X-ray angle range of ±45° to obtain a projected image. A region of interest was placed at the center of the bead image and the slice sensitivity profile (SSP) was obtained by acquiring pixel values in the z-direction. The full width at half maximum of the SSP was defined as the Z-resolution and the frequency response was obtained by the 1-D Fourier transform of the SSP. Results: Z-resolution increased with increasing bead diameter. However, there was no change in Z-resolution between 0.025 and 0.1 mm. The frequency response was similar to that of the Z-resolution, with a significant difference between 0.1 and 0.2 mm diameter. Conclusions: Z-resolution is dependent on the diameter of the bead, which should be selected considering the pixel size of the tomosynthesis image.