• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1124-1132
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• 2009

We proposed a new method to improve the resolution of elemental image set in the integral imaging system using super-resolution image reconstruction method. Adjacent elemental images have same image region which is projected from the common area of object. These projected images in the elemental image can be used for low resolution images of super-resolution method. Two methods for resolution improvement of elemental image set using super-resolution method are proposed. One is super-resolution among the elemental image sets and the other is among the elemental images. Simulation results are compared with resolution improved elemental image set using interpolated method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.147-154
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• 2018

In this paper, we propose methods to acquire enemy object information, which is frequently required in multi-resolution model based war game and affects the simulation performance. In the information request method, a multi-resolution model selects information of a specific resolution and provides them to external objects. In the information announcement method, a multi-resolution model announces all information, and external objects select information of a specific resolution. In the information sharing method, external objects obtains information of a specific resolution by inquiring all information of a multi-resolution model which are stored in a shared space. Simulation results show that the information sharing method is more efficient than the information request method and the information announcement method because the information acquisition is fast. In addition, the proposed methods will increase the efficiency of war game operation by shortening the time for acquiring enemy forces' information.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.74-85
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• 2018

The research investigated the interaction effect of video resolution and device luminance on the perceived characteristics, presence, and fatigue viewing virtual reality. Experiments were composed of mixed design, and the resolution and luminance were classified into three types, HD, 2K, and 4K, and 20, 60, and 100, respectively. Participants watched video of 6-minutes randomly, and responded to the questionnaire after watching each video. The results showed that no interaction effect existed. Meanwhile, there are statistically significant differences on the luminance of depth perception, the resolution of visual intervention, and the resolution of adjustment fatigue. Also, higher resolution and luminance showed higher cognitive function, presence and fatigue.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.72-79
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• 2007

In this paper, It propose a new restoration algorithm of high resolution, which is reconstructed to high resolution image using low resolution image informations. The proposed algorithm is constructed based on super resolution theory, it is consisted of progressive steps of the integration and construction. It reduced a lot of data-processing capacity and noise with integration through sub-pixel movement and wavelet basis through a higher resolution. As a result, it is shown that the main information is maintained and the error rate is improved. Using expansion fuzzy wavelet B-spline interpolation in stage of construction, it is confirmed that we can achieve smoothing image and good resolution without blur and block.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.540-541
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• 2018

In this paper, we propose an super-resolution method that improves resolution by using DWT (Discrete Wavelet Transform) for low resolution infra-red images. In this method, DWT is performed in a manner that does not reduce the resolution of an image input through an infra-red camera to generate sub-bands of the same resolution (LH, HL, and HH) And the original infra-red image is used to perform an inverse-DWT to obtain an infra-red image with improved resolution. Experimental results show that the mean SSIM value of the proposed method is 0.989861, which is about 0.004 higher than that of the conventional Bi-linear and Bi-cubic filters.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.38-43
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• 2017

Many studies about generating images of seabed using active sonar have been carried out but image resolution enhancement is still an important problem. Many methods have been proposed to improve sonar resolution and the approach using narrow beam width is commonly and widely applied to enhance azimuth resolution. Unfortunately, this has technical limitations to reduce the beam width. Therefore, signal processing techniques are essential to achieving higher azimuth resolution when an array with conventional beam width is employed. This paper proposes a new approach that utilizes overlapped beams to obtain higher resolution.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.105-111
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• 2014

Image resolution enhancement is a technique to generate high-resolution image through improving resolution of low-resolution obtained image. It is important to estimate correctly missing pixel value in low-resolution obtained image for image resolution enhancement. In this paper, multiple shortfall estimation method for image resolution enhancement is proposed. The proposed method estimate separate multiple shortfall by predictive degradation-restoration processing in sub-images of obtained image, and generate result image combining the estimated shortfall and interpolated obtained-image. Lastly, final reconstruction image is generated by deblurring of the result image. The experimental results demonstrate that the proposed method has the best results of all compared methods in objective image quality index: PSNR, SSIM, and FSIM. The quality of reconstructed image is superior to all compared methods, and the proposed method has better lower computational complexity than compared methods. The proposed method can be useful for image resolution enhancement.

• Journal of Digital Policy
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• v.1 no.2
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• pp.53-59
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• 2022

In this paper, we proposed a method using sub-edge information extracted through a hierarchical structure in the process of generating super resolution based on a single image. In order to improve the quality of super resolution, it is necessary to clearly distinguish the shape of each area while clearly expressing the boundary area in the image. The proposed method assists edge information of the image in deep learning based super resolution method to create an improved super resolution result while maintaining the structural shape of the boundary region, which is an important factor determining the quality in the super resolution process. In addition to the group convolution structure for performing deep learning based super resolution, a separate hierarchical edge accumulation extraction process based on high-frequency band information for sub-edge extraction is proposed, and a method of using it as an auxiliary feature is proposed. Experimental results showed about 1% performance improvement in PSNR and SSIM compared to the existing super resolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.