• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.355-359
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• 2009

In this paper, we describe self-training super-resolution. Our approach is based on example based algorithms. Example based algorithms need training images, and selection of those changes the result of the algorithm. Consequently it is important to choose training images. We propose self-training based super-resolution algorithm which use an input image itself as a training image. It seems like other example based super-resolution methods, but we consider training phase as the step to collect primitive information of the input image. And some artifacts along the edge are visible in applying example based algorithms. We reduce those artifacts giving weights in consideration of the edge direction. We demonstrate the performance of our approach is reasonable several synthetic images and real images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.23-31
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• 2012

This paper proposes a motion estimation-based super resolution algorithm to restore input low-resolution images of large movement into a super-resolution image. It is difficult to find the sub-pixel motion estimation in images of large movement compared to typical experimental images. Also, it has disadvantage which have high computational complexity to find reference images and candidate images using general motion estimation method. In order to solve these problems for the traditional two-dimensional motion estimation using the proposed registration threshold that satisfy the conditions based on the reference image is determined. Candidate image with minimum weight among the best candidates for super resolution images, the restoration process to proceed with to find a new image registration algorithm is proposed. According to experimental results, the average PSNR of the proposed algorithm is 31.89dB and this is better than PSNR of traditional super-resolution algorithm and it also shows improvement of computational complexity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.254-257
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• 2009

In this paper, we propose a learning-based super-resolution algorithm. In the proposed algorithm, a multi-resolution wavelet approach is adopted to perform the synthesis of local high-frequency features. To obtain a high-resolution image, wavelet coefficients of two dominant LH- and HL-bands are estimated based on wavelet frames. In order to prepare more efficient training sets, the proposed algorithm utilizes the LH-band and transposed HL-band. The training sets are then used for the estimation of wavelet coefficients for both LH- and HL-bands. Using the estimated high frequency bands, a high resolution image is reconstructed via the wavelet transform. Experimental results demonstrate that the proposed scheme can synthesize high-quality images.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.294-302
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• 2008

In this paper, we present a new image up-sampling method which registers low resolution images to the high resolution grid when Bayesian super-resolution image processing is performed. The proposed up-sampling method interpolates high-resolution pixels using high-frequency data lying in all the low resolution images, instead of up-sampling each low resolution image separately. The interpolation is based on B-spline non-uniform re-sampling, adjusted for the super-resolution image processing. The experimental results demonstrate the effects when different up-sampling methods generally used such as zero-padding or bilinear interpolation are applied to the super-resolution image reconstruction. Then, we show that the proposed hybird up-sampling method generates high-resolution images more accurately than conventional methods with quantitative and qualitative assess measures.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.412-420
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• 2020

This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.61-70
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• 2020

Convolutional Neural Networks (CNN) have been used extensively in recent times to solve image classification and segmentation problems. However, the use of CNNs in image super-resolution problems remains largely unexploited. Filter interpolation and prediction model methods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lot of the edge information are lost. In this paper, we analyze super resolution based on CNN and the wavelet transform super resolution method. We compare and analyze the performance according to the number of layers and the training data of the CNN.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.71-77
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• 2018

The optical time domain reflectometer (OTDR) is the most widely used method to monitor problems with currently installed optical fibers. The OTDR is an instrument designed to test the FTTx network and evaluates the physical properties of the fiber, such as transmission loss and connection loss. It is important to improve the spatial resolution in order to accurately grasp the optical path problems by using the OTDR. When the pulse width is less than twice the distance between the two reflectors, the signals reflected from the two reflectors are reflected without overlap, so that the reflected signal can be distinguished. However, when the pulse width is larger than twice the distance between the two reflectors, so that the reflected signal can not be distinguished. In order to overcome these limitations, this paper proposed a method of improving spatial resolution by applying a super resolution algorithm. As a result of the simulation, the resolution is improved when the super resolution algorithm is applied, and the event interval can be analyzed more precisely.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.