• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.137-143
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• 2013

The high resolution satellite images are used in many fields such as weather observation, remote sensing, military facilities monitoring, cultural properties protection etc. Although satellite images are obtained in same satellite imaging system, the satellite images are degraded depending on the condition of hardware(optical device, satellite operation altitude, image sensor, etc.). Due to the fact that changing the hardware of satellite imaging system is impossible for resolution enhancement of these degraded satellite after launching a satellite, therefore the method of resolution enhancement with satellite images is necessary. In this paper the resolution is enhances by using a Super Resolution(SR) algorithm. The SR algorithm is an algorithm to enhance the resolution of an image by uniting many low resolution images, so an output image has higher resolution than using other interpolation methods. But It is difficult to obtain many images of the same area. Therefore, to solve this problem, we applied SR after by applying the affine and projection transform. As a results, we found that the images applied SR after affine and projection transform have higher resolution than the images only applied SR.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.23-29
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• 2014

The emergence of high resolution satellite images and the evolution of spatial resolution facilitate various studies using high resolution satellite images. Above all, target detection algorithms are effective for monitoring of traffic flow and military surveillance and reconnaissance because vehicles, airplanes, and ships on broad area could be detected easily using high resolution satellite images. Recently, many satellites are launched from global countries and the diversity of satellite images are also increased. On the contrary, studies on comparison about the spatial resolution or target detection, especially, are insufficient in domestic and foreign countries. Therefore, in this study, effects of spatial resolution on target detection are analyzed using the PSO target detection algorithm. The resampling techniques such as nearest neighbor, bilinear, and cubic convolution are adopted to resize the original image into 0.5m, 1m, 2m, 4m spatial resolutions. Then, accuracy of target detection is assessed according to not only spatial resolution but also resampling method. As a result of the study, the resolution of 0.5m and nearest neighbor among the resampling methods have the best accuracy. Additionally, it is necessary to satisfy the criteria of 2m and 4m resolution for the detection of airplane and ship, respectively. The detection of airplane need more high spatial resolution than ship because of their complexity of shape. This research suggests the appropriate spatial resolution for the plane and ship target detection and contributes to the criteria of satellite sensor design.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.464-475
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• 2017

In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi-resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.

• Journal of Arbitration Studies
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• v.25 no.1
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• pp.3-23
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• 2015

As the Internet rapidly emerges as a speedy and cost-effective way of purchasing goods from overseas websites, the number of disputes arising out of overseas direct purchases also increases. In such situations, a disgruntled consumer might be left without an effective remedy. Providing an alternative approach to redress such grievances might assist in resolving such disputes and in increasing consumer confidence in e-commerce. Online Dispute Resolution (ODR) will allow consumers to solve their disputes without going to court, in a quick, low-cost, and simple way. It also helps to eliminate complex jurisdictional and choice-of-law problems. On the other hand, it has many problems such as having inadequate confidentiality and security, not being able to meet the "writing" requirement for arbitration of disputes, having difficulty in enforcing online arbitration agreements, having difficulties in enforcing online decisions and so on. This article investigates relationship online disputes and ODR and suggests ways that ODR can work best in resolving disputes arising out of overseas direct purchases. To expand the ODR system in online disputes, it is very important for domestic consumers to recognize the concept and usefulness of the Alternative Dispute Resolution (ADR) and ODR systems. The Korean government must also help consumers recognize the ADR mechanisms of dispute resolution by public campaign advertisement of ADR systems. Further education of dispute resolution in higher educational institutions is also required as well as assisting the KCAB with funds and the establishment of ADR Law.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.16-23
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• 1997

Among the main technologies to implement 3D TV succeeding HDTV, multi-viewpoint image display technique is rising as an important issue, which can display the viewpoint-dependent images corresponding to viewer's position. This paper presents a novel method that solves too much computational overload that is main drawback of previous methods. Using down sampling technique, multiresolution EPIs are made from multi-viewpoint image set and trace lines are detected in the lowest resolution EPI. The parameters of detected trace lines are transferred to higher resolution EPIs and revised by utilizing the information of the previous resolution EPI. This procedure is iterated until orignal resolution EPI. Using the proposed method, we have achieved the reduction of computational time and the robustness to noise in comparison to previous method.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.230-236
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• 2018

3D depth perception has played an important role in robotics, and many sensory methods have also proposed for it. As a photodetector for 3D sensing, single photon avalanche diode (SPAD) is suggested due to sensitivity and accuracy. We have researched for applying a SPAD chip in our fusion system of time-of-fight (ToF) sensor and stereo camera. Our goal is to upsample of SPAD resolution using RGB stereo camera. Currently, we have 64 x 32 resolution SPAD ToF Sensor, even though there are higher resolution depth sensors such as Kinect V2 and Cube-Eye. This may be a weak point of our system, however we exploit this gap using a transition of idea. A convolution neural network (CNN) is designed to upsample our low resolution depth map using the data of the higher resolution depth as label data. Then, the upsampled depth data using CNN and stereo camera depth data are fused using semi-global matching (SGM) algorithm. We proposed simplified fusion method created for the embedded system.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.101-110
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• 2021

In computer vision, single-image super resolution has been an area of research for a significant period. Traditional techniques involve interpolation-based methods such as Nearest-neighbor, Bilinear, and Bicubic for image restoration. Although implementations of convolutional neural networks have provided outstanding results in recent years, efficiency and single model multi-scalability have been its challenges. Furthermore, previous works haven't placed enough emphasis on real-number scalability. Interpolation-based techniques, however, have no limit in terms of scalability as they are able to upscale images to any desired size. In this paper, we propose a convolutional neural network possessing the advantages of the interpolation-based techniques, which is also efficient, deeming it suitable in practical implementations. It consists of convolutional layers applied on the low-resolution space, post-up-sampling along the end hidden layers, and additional layers on high-resolution space. Up-sampling is applied on a multiple channeled feature map via bicubic interpolation using a single model. Experiments on architectural structure, layer reduction, and real-number scale training are executed with results proving efficient amongst multi-scale learning (including scale multi-path-learning) based models.