A5101539587- Image and Signal Denoising Methods
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Advanced Image Fusion Techniques
- Acoustic Wave Phenomena Research
- Image Processing Techniques and Applications
- Biological Activity of Diterpenoids and Biflavonoids
- Medical Image Segmentation Techniques
- Educational Systems and Policies
- Digital Radiography and Breast Imaging
- Radiation Dose and Imaging
- Diverse Approaches in Healthcare and Education Studies
- Seismic Imaging and Inversion Techniques
- Plant biochemistry and biosynthesis
- Cell death mechanisms and regulation
- Technology and Data Analysis
- Transplantation: Methods and Outcomes
- Music Technology and Sound Studies
- Tracheal and airway disorders
- Chemotherapy-induced cardiotoxicity and mitigation
- Noise Effects and Management
- Species Distribution and Climate Change
- Adhesion, Friction, and Surface Interactions
Korea Institute of Civil Engineering and Building Technology
2025
Korea Advanced Institute of Science and Technology
2024
Incheon National University
2024
Ewha Womans University
2021-2023
Chosun University
2021-2022
York University
2020
Kwangwoon University
2018
Yonsei University
2015
Severance Hospital
2015
Keimyung University
2012-2013
This paper reviews the NTIRE 2020 challenge on real image denoising with focus newly introduced dataset, proposed methods and their results. The is a new version of previous 2019 that was based SIDD benchmark. collected validation testing datasets, hence, named SIDD+. has two tracks for quantitatively evaluating performance in (1) Bayer-pattern rawRGB (2) standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total 22 teams, proposing 24 methods, competed final phase...
Abstract Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) protocols while keeping the radiation dose as low reasonably achievable. In medical domain, IQA based on how well an provides a useful and efficient presentation necessary for physicians make diagnosis. Moreover, results should be consistent with radiologists’ opinions quality, which accepted gold standard IQA. As such, goals of are greatly different from those natural addition, lack pristine...
Low-dose computed tomography (LDCT) is crucial due to the risk of radiation exposure patients. However, high noise level in LDCT images may reduce image quality, leading a less accurate diagnosis. Deep learning technology, especially supervised methods, has recently been widely accepted as powerful tool for denoising tasks. methods require numerous paired datasets and high-quality pristine CT images, which are rarely available real-world clinical scenarios. This study presents an...
Background: Sclerostin is a secreted Wnt inhibitor produced almost exclusively by osteocytes, which inhibits bone formation.Aromatase inhibitors (AIs), reduce the conversion of steroids to estrogen, are used treat endocrine-responsive breast cancer.As AIs lower estrogen levels, they increase turnover and mass.We analyzed changes in serum sclerostin levels Korean women with cancer who were treated an AI.Methods: We included postmenopausal (n=90; mean age, 57.7 years) AI, compared them healthy...
Gastric cancer is a malignant tumor with high incidence and mortality rate worldwide. Nevertheless, anticancer drugs that can be used for gastric treatment are limited. Therefore, it important to develop targeted the of cancer. Dehydroabietic acid (DAA) diterpene found in tree pine. Previous studies have demonstrated DAA inhibits cell proliferation by inducing apoptosis. However, we did not know how cells through In this study, attempted identify genes induce cycle arrest death, as well...
Abstract Background Although low‐dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure patients, the reconstructed CT images tend have noise, which impedes accurate diagnosis. Recently, deep neural networks using convolutional noise shown considerable improvement. However, they need a large number of paired normal‐ and fully train network via supervised learning methods. Purpose To propose an unsupervised two‐step training...
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early algorithms were primarily optimized to obtain an accurate image with low distortion between the denoised and reference full-dose at cost of yielding overly smoothed unrealistic CT image. Recent research has sought preserve fine details images high perceptual quality, which been accompanied by a decrease objective quality due trade-off distortion. We pursue network that can generate...
Objective image quality metrics (IQMs) are widely developed and utilized, considering that they can lead to optimal radiation doses in computed tomography (CT) imaging. However, how well these IQMs relate a radiologist's perception of subjective quality, which is the gold standard for assessing diagnostic has not been fully explored. Therefore, this study, we aim analyze relationship between objective metrics. We compared 13 full-reference no-reference IQMs, including root mean square error,...
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based CT denoising algorithms were primarily based on supervised learning. However, learning requires a large number of training samples, which is impractical real-world scenarios. To address this problem, we propose novel unsupervised domain adaptation approach for This proposed framework adapts the network pretrained with paired low- and normal-dose phantom images (source...
The use of low-dose x-ray fluoroscopy imaging has been found to be effective in reducing radiation exposure during prolonged procedures that may result high doses patients. However, the noise generated by protocol can degrade quality fluoroscopic images and impact clinical diagnostic accuracy. This paper proposes a novel framework for denoising algorithm recover extremely small details texture edges denoised images. While existing deep learning–based approaches have shown promising...
Many technologies requiring three-dimensional (3D) information, such as augmented reality, virtual robots, and autonomous vehicles, are developing rapidly. Among them, 3D reconstruction from a monocular image is challenging because of the lack information in images large-scale errors wide, low-texture regions found them. However, modern obtained outdoors contains many artificial environmental stereo cues; hence, we regard this Manhattan world. In study, find vanishing points, which effective...
The contraction and expansion noise of a refrigerator are investigated, some effective methods proposed to reduce the level occurrence frequency noise. First, is measured estimate spectrum Second, sound visualization was conducted using an acoustic camera determine location source. From results, it observed that internal part mainly producing third shelf in freezer room. A method acceleration on source introduced analyze precisely accommodate experimental convenience. Noise reduction such as...
In this study, the noise mapping simulation is executed to design an effective barrier reducing levels of a school site.The geographical features ambient site and buildings are modelled in detail order consider sound propagation, deflection, absorption phenomena etc.The main source, power level expressway, estimated on basis measured at several points performed by using ENPro, environmental prediction program based ISO 9613 analysis effectiveness barrier.Consequently, designed meet standard...
무인항공기 (UAV, Unmanned Aerial Vehicle)에 탑재되는 다양한 센서들 중에서 GPS (Global Positioning System) 수신기는 신호를 기반으로 정지비행 (hovering flight), 경로비행 (waypoint flight) 등 임무의 수행을 돕는다. GPS신호가 원활하게 수신되는 환경에서는 수신기를 활용할 수 있지만, 최근에 ...
As the increasing number of cameras adopt fisheye lenses for sensing large area at once, an effective stereo vision algorithm is required images. The exterior part image distorted so that existing planar may not work properly. In this paper, we propose a method in which mapped to sphere and feature detection performed on sphere. With experimental results, proposed spherical shows better performance than algorithms.
Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering dose inevitably increases random noise x-ray images, resulting poor diagnostic image quality, which requires reduction for accurate diagnosis. Also, case of non-static objects, blurred due motion. The most-used denoiser recursive filter (RF) preserves details well when applied temporal data, but it vulnerable motion blur. Existing convolutional neural network (CNN)-based...