• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2204-2212
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• 2022

Recently, the demand for alpha imaging detectors for quantifying the distributions of alpha particles has increased in various fields. This study aims to reconstruct a high-resolution image from an alpha imaging detector by applying a super-spatial resolution method combined with the maximum-likelihood expectation maximization (MLEM) algorithm. To perform the super-spatial resolution method, several images are acquired while slightly moving the detector to predefined positions. Then, a forward model for imaging is established by the system matrix containing the mechanical shifts, subsampling, and measured point-spread function of the imaging system. Using the measured images and system matrix, the MLEM algorithm is implemented, which converges towards a high-resolution image. We evaluated the performance of the proposed method through the Monte Carlo simulations and phantom experiments. The results showed that the super-spatial resolution method was successfully applied to the alpha imaging detector. The spatial resolution of the resultant image was improved by approximately 12% using four images. Overall, the study's outcomes demonstrate the feasibility of the super-spatial resolution method for the alpha imaging detector. Possible applications of the proposed method include high-resolution imaging for alpha particles of in vitro sliced tissue and pre-clinical biologic assessments for targeted alpha therapy.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1141-1154
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• 2006

Orographic effect is one of the important factors to induce Local circulations and to make atmospheric turbulence, so it is necessary to use the exact topographic data for prediction of local circulations. In order to clarify the sensitivity of the spatial resolution of topography data, numerical simulations using several topography data with different spatial resolution are carried out under stable and unstable synoptic conditions. The results are as follows: 1) Influence of topographic data resolution on local circulation tends to be stronger at simulation with fine grid than that with coarse grid. 2) The hight of mountains in numerical model become mote reasonable with high resolution topographic data, so the orographic effect is also emphasized and clarified when the topographic data resolution is higher. 2) The higher the topographic resolution is, the stronger the mountain effect is. When used topographic data resolution become fine, topography in numerical model becomes closer to real topography. 3) The topographic effect tends to be stronger when atmospheric stability is strong stable. 4) Although spatial resolution of topographic data is not fundamental factor for dramatic improvement of weather prediction accuracy, some influence on small scale circulation can be recognized, especially in fluid dynamic simulation.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.70.4-70.4
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• 2019

Image super-resolution (SR) is a technique that enhances the resolution of a low resolution image. In this study, we use three SR models (RCAN, ProSRGAN and Bicubic) for enhancing solar SDO/HMI magnetograms using deep learning. Each model generates a high resolution HMI image from a low resolution HMI image (4 by 4 binning). The pixel resolution of HMI is about 0.504 arcsec. Deep learning networks try to find the hidden equation between low resolution image and high resolution image from given input and the corresponding output image. In this study, we trained three models with HMI images in 2014 and test them with HMI images in 2015. We find that the RCAN model achieves higher quality results than the other two methods in view of both visual aspects and metrics: 31.40 peak signal-to-noise ratio(PSNR), Correlation Coefficient (0.96), Root mean square error (RMSE) is 0.004. This result is also much better than the conventional bi-cubic interpolation. We apply this model to a full-resolution SDO/HMI image and compare the generated image with the corresponding Hinode NFI magnetogram. As a result, we get a very high correlation (0.92) between the generated SR magnetogram and the Hinode one.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.105-113
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• 2019

Physically-based fluid simulation takes a lot of time for high resolution. To solve this problem, there are studies that make up the limitation of low resolution fluid simulation by using deep running. Among them, Super-resolution, which converts low-resolution simulation data to high resolution is under way. However, traditional techniques require to the entire space where there are no density data, so there are problems that are inefficient in terms of the full simulation speed and that cannot be computed with the lack of GPU memory as input resolution increases. In this paper, we propose a new method that divides and classifies 2D smoke simulation data into the space using the quad tree, one of the spatial partitioning methods, and performs Super-resolution only required space. This technique accelerates the simulation speed by computing only necessary space. It also processes the divided input data, which can solve GPU memory problems.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.7-13
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• 2020

Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.81-95
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• 1996

In this paper, we propose a motio estiamtion algorithm using low-resolution quantization to reduce the computation of the full search algorithm. The proposed algorithm consists of the low-resolution search which determins the candidate motion vectors by comparing the low-resolution image and the full-resolution search which determines the motion vector by comparing the full-resolution image on the positions of the candidate motion vectors. The low-resolution image is generated by subtracting each pixel value in the reference block or the search window by the mean of the reference block, and by quantizing it is 2-bit resolution. The candidate motion vectors are determined by counting the number of pixels in the reference block whose quantized codes are unmatched to those in the search window. Simulation results show that the required computational cost of the proposed algorithm is reduced to 1/12 of the full search algorithm while its performance degradation is 0.03~0.12 dB.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.55-62
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• 2000

In this paper, an algorithm for determining the optimal resolution has been described for measuring the actual length of circular objects. As the resolution gets higher, the measurement error in general becomes smaller because of the reduced distance per pixel. However, the higher resolution makes circular objects enlarged, which may produce an ill-conditioned system. That is, a small error in the boundary positions may result in a large error in the analysis of the circular objects. Taking this fact into account, a new measure is proposed to determine the optimal resolution. The actual errors have been calculated with various resolutions and the resolution with the minimum error has been decided as the optimal resolution. The analysis using various circles with different sizes indicates that the minimum measurement error is obtained when the whole circle appears in the screen as large as possible, regardless of the size of circle. The experimental results using real images have verified the validity of our analysis.

• Smart Media Journal
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• v.8 no.4
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• pp.64-71
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• 2019

The performance of an OMR (Optical Music Recognition) system is usually determined by the characterizing features of the input music score images. Low resolution is one of the main factors leading to degraded image quality. In this paper, we handle the low-resolution problem using the super-resolution technique. We propose the use of a deep neural network with instance normalization to improve the quality of music score images. We apply instance normalization which has proven to be beneficial in single image enhancement. It works better than batch normalization, which shows the effectiveness of shifting the mean and variance of deep features at the instance level. The proposed method provides an end-to-end mapping technique between the high and low-resolution images respectively. New images are then created, in which the resolution is four times higher than the resolution of the original images. Our model has been evaluated with the dataset "DeepScores" and shows that it outperforms other existing methods.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1311-1317
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• 2021

In this study, an airborne alpha detection system, which consists of a passivated implanted planar silicon (PIPS) detector and an air filter, was developed. A collimator applied to the alpha detection system showed an enhancement in resolution and a degradation in detection efficiency. The resolution and detection efficiency were compared and analyzed to evaluate the performance of the collimator. Thus, the resolution was found to be more important than the efficiency as a determining factor of the detection system performance, from the viewpoint of radionuclide identification. The performance was evaluated on three properties of the collimator: hole shape, hole length, and the ratio between the hole and frame pitches. From the hole shape performance evaluation, a hexagonal collimator showed the highest resolution. Further, the collimator with a hole pitch of 14 mm was found to have the highest resolution while that with a frame pitch of 4-6 mm (i.e., 1.2-1.4 times longer than the hole pitch) showed the highest resolution.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.218-224
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• 1998

The coefficients of variation obtained from three typical vegetation indices of eight levels of multi-spatial resolution images in urban areas were employed to identify the optimum spatial resolution in terms of maintaining information quality. These multi-spatial resolution images were prepared by degrading 1 meter simulated, 16 meter ADEOS/AVNIR, and 30 meter Landsat-TM images. Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI) and Soil Adjusted Ratio Vegetation Index (SARVI) were applied to reduce data redundancy and compare the characteristics of multi-spatial resolution image of vegetation indices. The threshold point on the curve of the coefficient of variation was defined as the optimum resolution level for the analysis with multi-spatial resolution image sets. Also, the results from the image segmentation approach of region growing to extract man-made features were compared with these multi-spatial resolution image sets.