• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.363-371
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• 2014

In this paper, a new super-resolution algorithm is proposed by using successive frames for generating high-resolution frames with better quality than those generated by other conventional interpolation methods. Generally, each frame used for super-resolution must only have global translation and motions of sub-pixel unit to generate good result. However, the newly proposed MSR algorithm in this paper is exempt from such constraints. The proposed algorithm consists of three main processes; motion estimation for image registration, normalization of motion vectors, and pattern analysis of edges. The experimental results show that the proposed algorithm has better performance than other conventional algorithms.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.703-712
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• 2019

Single image Super-Resolution (SISR) aims to generate a visually pleasing high-resolution image from its degraded low-resolution measurement. In recent years, deep learning - based super - resolution methods have been actively researched and have shown more reliable and high performance. A typical method is WaveletSRNet, which restores high-resolution images through wavelet coefficient learning based on feature maps of images. However, there are two disadvantages in WaveletSRNet. One is a big processing time due to the complexity of the algorithm. The other is not to utilize feature maps efficiently when extracting input image's features. To improve this problems, we propose an efficient single image super resolution method, named RDB-WaveletSRNet. The proposed method uses the residual dense block to effectively extract low-resolution feature maps to improve single image super-resolution performance. We also adjust appropriated growth rates to solve complex computational problems. In addition, wavelet packet decomposition is used to obtain the wavelet coefficients according to the possibility of large scale ratio. In the experimental result on various images, we have proven that the proposed method has faster processing time and better image quality than the conventional methods. Experimental results have shown that the proposed method has better image quality by increasing 0.1813dB of PSNR and 1.17 times faster than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.54-63
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• 2011

This paper proposes a fast learning-based interpolation algorithm to up-scale an input low-resolution image into a high-resolution image. In conventional learning-based super-resolution, a certain relationship between low-resolution and high-resolution images is learned from various training images and a specific high frequency synthesis information is derived. And then, an arbitrary low resolution image can be super-resolved using the high frequency synthesis information. However, such super-resolution algorithms require heavy memory space to store huge synthesis information as well as significant computation due to two-dimensional matching process. In order to mitigate this problem, this paper presents one-dimensional patch-based learning and synthesis. So, we can noticeably reduce memory cost and computational complexity. Simulation results show that the proposed algorithm provides higher PSNR and SSIM of about 0.7dB and 0.01 on average, respectively than conventional bicubic interpolation algorithm.

• 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.403.1-403.1
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• 2014

In conventional far-field microscopy, two objects separated closer than approximately half of an emission wavelength cannot be resolved, because of the fundamental limitation known as Abbe's diffraction limit. During the last decade, several super-resolution methods have been developed to overcome the diffraction limit in optical imaging. Among them, super-resolution optical fluctuation imaging (SOFI) developed by Dertinger et al [1], employs the statistical analysis of temporal fluorescence fluctuations induced by blinking phenomena in fluorophores. SOFI is a simple and versatile method for super-resolution imaging. However, due to the uncontrollable blinking of fluorophores, there are some limitations to using SOFI for several applications, including the limitations of available blinking fluorophores for SOFI, a requirement of using a high-speed camera, and a low signal-to-noise ratio. To solve these limitations, we present a new approach combining SOFI with speckle pattern illumination to create illumination-induced optical fluctuation instead of blinking fluctuation of fluorophore.. This technique effectively overcome the limitations of the conventional SOFI since illumination-induced optical fluctuation is possible to control unlike blinking phenomena of fluorophore. And we present the sub-diffraction resolution image using SOFI with speckle illumination.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.19-26
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• 2020

In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.203-206
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• 2002

One of the ever-increasing demands on the performances of heterodyne interferometers is to improve the measurement resolution, of which current state-of-the-art reaches the region of sub-nanometers. We propose a new scheme of phase-measuring electronics that reduces the measurement resolution without further increase in clock speed. Our scheme adopts a super-heterodyne technique that lowers the original beat frequency to a level of 1 MHz by mixing it with electrically generated reference signal. The technique enables us to measure the phase of Doppler shift with a resolution of 1.58 nanometer at a sampling rate of 1 MHz. To avoid the undesirable decrease in the maximum measurable speed caused by the lowered beat frequency, a special from of frequency up-down counting technique is combined with the super-heterodyning. This alloys performing required phase unwrapping simply by using programmable digital gates without 2$\pi$ ambiguities up to the maximum velocity of 2.35 m/s.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1141-1162
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• 2024

Humidity is an important parameter in meteorology and is closely related to weather, human health, and the environment. Due to the limitations of the number of observation stations and other factors, humidity data are often not as good as expected, so high-resolution humidity fields are of great interest and have been the object of desire in the research field and industry. This study presents a novel super-resolution algorithm for humidity fields based on the Wasserstein generative adversarial network(WGAN) framework, with the objective of enhancing the resolution of low-resolution humidity field information. WGAN is a more stable generative adversarial networks(GANs) with Wasserstein metric, and to make the training more stable and simple, the gradient cropping is replaced with gradient penalty, and the network feature representation is improved by sub-pixel convolution, residual block combined with convolutional block attention module(CBAM) and other techniques. We evaluate the proposed algorithm using ERA5 relative humidity data with an hourly resolution of 0.25°×0.25°. Experimental results demonstrate that our approach outperforms not only conventional interpolation techniques, but also the super-resolution generative adversarial network(SRGAN) algorithm.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.5-18
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• 2023

As the development of drones and sensors accelerates, new services and values are created by fusing data acquired from various sensors mounted on drone. However, the construction of spatial information through data fusion is mainly constructed depending on the image, and the quality of data is determined according to the specification and performance of the hardware. In addition, it is difficult to utilize it in the actual field because expensive equipment is required to construct spatial information of high-quality. In this study, super-resolution was performed by applying deep learning to low-resolution images acquired through RGB and THM cameras mounted on a drone, and quantitative evaluation and feature point extraction were performed on the generated high-resolution images. As a result of the experiment, the high-resolution image generated by super-resolution was maintained the characteristics of the original image, and as the resolution was improved, more features could be extracted compared to the original image. Therefore, when generating a high-resolution image by applying a low-resolution image to an super-resolution deep learning model, it is judged to be a new method to construct spatial information of high-quality without being restricted by hardware.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.463-468
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• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.