• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.13-18
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• 2008

Purpose: The image processing method due to the algorism which is various portion nuclear medical image decision is important it makes holds. The purpose of this study is it applies hereupon new image processing method SIEMENS (made by Pixon co.) Onco. flash processing reconstruction and the comparison which use the image control technique of existing the clinical usefulness it analyzes with it evaluates. Materials & Methods: 1. Whole body bone scan-scan speed 20 cm/min, 30 cm/min & 40 cm/min blinding test 2. Bone static spot scan-regional view 200 kcts, 400 kcts for chest, pelvis, foot blinding test 3. 4 quadrant-bar phantom-20000 kcts visual evaluation 4. LSF-FWHM resolution comparison ananysis. Results: 1. Raw data (20 cm/min) & processing data (30 cm/min)-similar level image quality 2. Low count static image-image quality clearly improved at visual evaluation result. 3. Visual evaluation by quadrant bar phantom-rising image quality level 4. Resolution comparison evaluation (FWHM)-same difference from resolution comparison evaluation Conclusion: The study which applies a new method Onco. flash processing reconstruction, it will be able to confirm the image quality improvement which until high level is clearer the case which applies the method of existing better than. The new reconstruction improves the resolution & reduces the noise. This enhances the diagnostic capabilities of such imagery for radiologists and physicians and allows a reduction in radiation dosage for the same image quality. Like this fact, rising of equipment availability & shortening the patient waiting move & from viewpoint of the active defense against radiation currently becomes feed with the fact that it will be the useful result propriety which is sufficient in clinical NM.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3144-3164
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• 2019

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal ($0^{\circ}$) face sample is available in gallery and seven poses ($0^{\circ}$, ${\pm}30^{\circ}$, ${\pm}35^{\circ}$, and ${\pm}45^{\circ}$) as a probe on the LFW and the CMU Multi-PIE databases.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.65-72
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• 2005

This paper proposes an image display system that reduces the core performance of the processor allocated in the image display, thereby enabling the use of a less expensive processor with a low performance. Essentially, the proposed system supports an image display function for a high resolution in the module of an electronic picture frame (EPF) using a low-performance processor based on converting high definition (HD) image data at a 15Hz frame rate into HD image data at a 60Hz frame rate for use in a digital TV system. As a result, the proposed system can reduce the processor performance to a level corresponding to an image display with a low frame rate, thereby reducing the product cost and allowing various additional functions. Finally, the proposed system is implemented to confirm effectiveness.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.46-56
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• 2016

In order to analyze the surface temperature in accordance with the surface material, surface temperatures between Thermal InfraRed Image (TIRI) and Landsat 8 satellite observed at the commercial area (Gwanghwamun) and residential area (Jungnang) are compared. The surface temperature from TIRI had applied atmospheric correction and compared with that from Landsat 8. The surface temperatures from Landsat 8 at Gwanghwamun and Jungnang are underestimated in comparison with that from TIRI. The difference of surface temperature between the two methods is greater in summer than in winter. When the analysis area was divided into detailed regions, depending on the material and the position of the surface, correlation of surface temperature between TIRI with Landsat 8 is as low as 0.29 (Gwanghwamun) and 0.18 (Jungnang), respectively. The results were caused from the resolution difference between the two methods. While the surface temperatures of each zone from Landsat 8 were observed almost constant, high-resolution TIRI observed relatively precise surface temperatures. When the each area was averaged as one space, correlation of surface temperature between TIRIs and Landsat 8 is more than 0.95. The spatially averaged surface temperature is higher at Jungnang, representing residential areas, than at Gwanghwamun, representing commercial areas. As a result, the observation of high resolution is required in order to observe the precise surface temperature. This is because it appears that the spatial distribution of the various surface temperature in the range of micro-scale according to the conditions of the ground surface.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.517-534
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• 2019

In abrupt fire disturbances, high quality images suitable for wildfire damage assessment can be difficult to acquire. Quantifying wildfire burn area and severity are essential measures for quick short-term disaster response and efficient long-term disaster restoration. Planetscope (PS) imagery offers 3 m spatial and daily temporal resolution, which can overcome the spatio-temporal resolution tradeoff of conventional satellites, albeit at the cost of spectral resolution. This study investigated the potential of augmenting PS imagery by integrating the spectral information from Sentinel-2 (S2) differenced Normalized Burn Ratio (dNBR) to PS differenced Normalized Difference Vegetation Index (dNDVI) using histogram matching,specifically for wildfire burn area and severity assessment of the Okgye wildfire which occurred on April 4th, 2019. Due to the difficulty in acquiring reference data, the results of the study were compared to the wildfire burn area reported by Ministry of the Interior and Safety. The burn area estimates from this study demonstrated that the histogram-matched (HM) PS dNDVI image produced more accurate burn area estimates and more descriptive burn severity intervals in contrast to conventional methods using S2. The HM PS dNDVI image returned an error of only 0.691% whereas the S2 dNDVI and dNBR images overestimated the wildfire burn area by 5.32% and 106%, respectively. These improvements using PS were largely due to the higher spatial resolution, allowing for the detection of sparsely distributed patches of land and narrow roads, which were indistinguishable using S2 dNBR. In addition, the integration of spectral information from S2 in the PS image resolved saturation effects in areas of low and high burn severity.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.96-107
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• 1996

In case of remote image retrieval via shared network or low speed link in order to make a decision for target image problems such as transmission delay are encountered. In this paper browsing and optimization techniques are proposed for fast retrieval of Image by the multiresolution representation and progressive transmission. The proposed network model was analyzed and evaluated for system's performance improvement. Interactive user-system using several multiresolution representation has shown better performance in transmission delay minimization over the single resolution image retrieval system.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.661-664
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• 1998

Since the region segmentation at high resolution contains most of viable semantic object contours in an image, the bottom-up approach for image segmentation is appropriate for the application such as MPEG-4 which needs to preserve semantic object contours. However, the conventioal region merging methods, that follow the region segmentation, have poor performance in keeping low-contrast semantic object contours. In this paper, we propose an image segmentation algorithm based on classified region merging. The algorithm pre-segments an image with a large number of small regions, and also classifies it into several classes having similar gradient characteristics. Then regions only in the same class are merged according to the boundary weakness or statisticsal similarity. The simulation result shows that the proposed image segmentation preserves semantic object contours very well even with a small number of regions.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.147-155
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• 2010

Landcover information of access-denied area was extracted from low-medium and high resolution satellite image. Training for supervised classification was performed to refer visually by landcover map which is made and distributed from The Ministry of Environment. The classification result was compared by relating data of FACC land classification system. As we rasterize digital military map with same pixel size of satellite classification, the accuracy test was performed by image to image method. In vegetation case, ancillary data such as NDVI and image for seasons are going to improve accuracy. FACC code of FDB need to recognize the properties which can be automated.