• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.93-98
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• 2023

Recently, as the size of display devices tends to increase and taking pictures with smart phones has become commonplace, the need for taking high-resolution pictures with smart phones is increasing. However, when the lens size of a camera is limited, such as in a smartphone, there is a physical limit to increasing the resolution of a photo. This paper is about a technique for increasing the resolution of a picture even when using a small-sized lens like a smartphone camera. It is to take multiple pictures while moving the smartphone, and to increase the resolution by combining these pictures into one picture. First of all, two pictures were taken while moving the smartphone horizontally for the 2D picture. Processes such as camera matrix estimation, and homograph inverse transformation were performed using OpenCV, and the resolution was improved by synthesizing one picture. It was confirmed that the resolution was improved in parts such as oblique lines or arcs on several test pictures.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.237-240
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• 2020

The fine-tuned neural network (NN) model for a whole temporal portion in a video does not always yield the best quality (e.g., PSNR) performance over all regions of each frame in the temporal period. For certain regions (usually homogeneous regions) in a frame for super-resolution (SR), even a simple bicubic interpolation method may yield better PSNR performance than the fine-tuned NN model. When there are multiple NN models available at the receivers where each NN model is trained for a group of images having a specific category of image characteristics, the performance of Quality enhancement can be improved by selectively applying an appropriate NN model for each image region according to its image characteristic category to which the NN model was dedicatedly trained. In this case, it is necessary to signal which NN model is applied for each region. This is very advantageous for image restoration and quality enhancement (IRQE) applications at user terminals with limited computing capabilities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.185-191
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• 2022

In this study, we propose a novel super-resolution neural network based on U-Net, residual neural network, and sub-pixel convolution. To prevent the loss of detailed information due to the max pooling of U-Net, we propose down-sampling and connection using sub-pixel convolution. This uses all pixels in the filter, unlike the max pooling that creates a new feature map with only the max value in the filter. As a 2×2 size filter passes, it creates a feature map consisting only of pixels in the upper left, upper right, lower left, and lower right. This makes it half the size and quadruple the number of feature maps. And we propose two methods to reduce the computation. The first uses sub-pixel convolution, which has no computation, and has better performance, instead of up-convolution. The second uses a layer that adds two feature maps instead of the connection layer of the U-Net. Experiments with a banchmark dataset show better PSNR values on all scale and benchmark datasets except for set5 data on scale 2, and well represent local area patterns.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.180-191
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• 2016

When up-scaling algorithms for visible light images are applied to infrared (IR) images, they rarely work because IR images are usually blurred. In order to solve such a problem, this paper proposes an up-scaling algorithm for IR images. We employ adaptive dynamic range encoding (ADRC) as a simple classifier based on the observation that IR images have weak details. Also, since human visual systems are more sensitive to edges, our algorithm focuses on edges. Then, we add pre-processing in learning phase. As a result, we can improve visibility of IR images without increasing computational cost. Comparing with Anchored neighborhood regression (A+), the proposed algorithm provides better results. In terms of just noticeable blur, the proposed algorithm shows higher values by 0.0201 than the A+, respectively.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.94-100
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• 2011

Purpose: Basal/Acetazolamide-challenged brain perfusion SPECT is very useful to assess cerebral perfusion and vascular reserve. However, as there is a trade off between sensitivity and spatial resolution in the selection of collimator, the selection of optimal collimator is crucial. In this study, we examined three collimators to select optimal one for 1-day brain perfusion SPECT. Materials and Methods: Three collimators, low energy high resolution-parallel beam (LEHR-par), ultra resolution-fan beam (LEUR-fan) and super fine-fan beam (LESFR-fan), were tested for 1-day imaging using Triad XLT 9 (TRIONIX). The SPECT images of Hoffman 3D brain phantom filled with 99mTc of 170 MBq and a normal volunteer were acquired with a protocol of 50 kcts/frame and detector rotation of 3 degree. Filterd backprojection (FBP) reconstruction with Butterworth filter (cut off frequencies, 0.3 to 0.5) was performed. The quantitative and qualitative assessments for three collimators were performed. Results: The blind tests showed that LESFR-fan provided the best image quality for Hoffman brain phantom and the volunteer. However, images for all the collimator were evaluated as 'acceptable'. On the other hand, in order to meet the equivalent signal-to-noise ratio (SNR), total acquisition time or radioactivity dose for LESFR-fan must have been increased up to almost twice of that for LEUR-fan and LEHR-par. The volunteer test indicated that total acquisition time could be reduced approximately by 10 to 14 min in clinical practice using LEUR-fan and LEHR-par without significant loss on image quality, in comparison with LESFR-fan. Conclusion: Although LESFR-fan provides the best image quality, it requires significantly more acquisition time than LEUR-fan and LEHR-par to provide reasonable SNR. Since there is no significant clinical difference between three collimators, LEUR-fan and LEHR-par can be recommended as optimal collimators for 1-day brain perfusion imaging with respect to image quality and SNR.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.102-105
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• 2019

본 논문에서는 심층 신경망 검색 방법을 사용하여 이미지 고해상도화를 위한 심층 신경망을 설계하는 방법을 구현하였다. 일반적으로 이미지 고해상도화, 잡음 제거 및 번짐 제거를 위한 심층신경망 구조는 사람이 설계하였다. 최근에는 이미지 분류 등 다른 영상처리 기법에서 사용하는 심층 신경망 구조를 검색하기 위한 방법이 연구되었다. 본 논문에서는 강화학습을 사용하여 이미지 고해상도화를 위한 심층 신경망 구조를 검색하는 방법을 제안하였다. 제안된 방법은 policy gradient 방법의 일종인 REINFORCE 알고리즘을 사용하여 심층 신경망 구조를 출력하여 주는 제어용 RNN(recurrent neural network)을 학습하고, 최종적으로 이미지 고해상도화를 잘 실현할 수 있는 심층 신경망 구조를 검색하여 설계하였다. 제안된 심층 신경망 구조를 사용하여 이미지 고해상도화를 구현하였고, 약 36.54dB 의 피크 신호 대비 잡음 비율(PSNR)을 가지는 것을 확인할 수 있었다.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.1
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• pp.30-35
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• 2004

We have carried out a basic experiment in order to develope a super high-resolution optical microscope which transcend the limitation of diffraction and the wavelength of lightwave. The image of this scope is composed by measuring the evanescent wave which is localized on the surface of the testing materials. A detecting probe was fabricated with a single mode optical fiber to be sharpened by the chemical etching, and drived by PZT. The standing wave of $0.33\mu\textrm{m}$ wavelength evanescent wave which was generated from the $0.78\mu\textrm{m}$-wavelength semiconductor laser was detected by the $0.5\mu\textrm{m}$-thickness optical fiber probe.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.109-110
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• 2007

본 논문에서는 베이시안 초해상도 영상처리시 저해상도 영상들을 고해상도 격자에 맞게 정합해서 업샘플링(upsampling)을 하는 새로운 방식에 대해 제안한다. 제안하는 업샘플링 방식은 각 장을 따로 보간하는 방식과 달리 여러 저해상도 영상의 고주파 정보가 고해상도 영상 격자의 모든 위치에 적절히 영향을 미칠 수 있도록 여러 장의 저해상도 영상의 고주파 정보를 함께 사용하여 보간한다. 보간하는 방법은 B-스플라인 (B-Spline) 기반 비정규 리샘플링(non-uniform resampling)을 기반으로 초해상도 영상처리에 맞도록 적용한다. 실험결과를 통해 일반적으로 적용되는 0-삽입(zero-padding) 업샘플링 방식과 쌍일차 보간법(bilinear interpolation) 등을 적용할 때의 효과를 살펴보고, 제안하는 방식이 일반적인 방식을 사용하는 것에 비해 정량적, 정성적으로 고해상도 정보를 더 정확히 생성해내는 것을 확인한다.