• Title/Summary/Keyword: Spatial Representative Noise Levels

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The Spatial Statistical Relationships between Road-traffic Noise and Urban Components Including Population, Building, Road-traffic and Land-use (공간통계모형을 이용한 도로 소음과 도시 구성 요소의 관계 연구)

  • Ryu, Hunjae;Park, In Kwon;Chang, Seo Il;Chun, Bum Seok
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.24 no.4
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    • pp.348-356
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    • 2014
  • To understand the relationship between road-traffic noise and urban components such as population, building, road-traffic and land-use, the city of Cheongju that already has road-traffic noise maps of daytime and nighttime was selected for this study. The whole area of the city is divided into square cells of a uniform size and for each cell, the urban components are estimated. A spatial representative noise level for each cell is determined by averaging out population-weighted facade noise levels for noise exposure population within the cell during nighttime. The relationship between the representative noise level and the urban components is statistically modeled at the cell level. Specially, we introduce a spatial auto regressive model and a spatial error model that turns out to explain above 85 % of the noise level. These findings and modeling methods can be used as a preliminary tool for environmental planning and urban design in modern cities in consideration of noise exposure.

Evaluation of Noise Level and Blind Quality in CT Images using Advanced Modeled Iterative Reconstruction (ADMIRE) (고급 모델 반복 재구성법 (ADMIRE)을 사용한 CT 영상에서의 노이즈 레벨 및 블라인드 화질 평가)

  • Shim, Jina;Kang, Seong-Hyeon;Lee, Youngjin
    • Journal of the Korean Society of Radiology
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    • v.16 no.3
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    • pp.203-209
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    • 2022
  • One of the typical methods for lowering radiation dose while maintaining image quality of computed tomography (CT) is the use of model-based iterative reconstruction (MBIR). This study is to evaluate the image quality by adjusting the strength of the advanced modeled iterative reconstruction (ADMIRE), which is well known as a representative model of MBIR. The study was conducted using phantom, and CT images were obtained while adjusting the strength of ADMIRE in units of 1 to 5. Quantitative evaluation includes noise levels using coefficient of variation (COV) and contrast to noise ratio (CNR), as well as natural image quality evaluation (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE). As a result, in both noise level and blind quality evaluation results, the higher the strength of ADMIRE, the better the results were derived. In particular, it was confirmed that COV and CNR were improved 1.89 and 1.75 times at ADMIRE 5 compared to ADMIRE 1, respectively, and NIQE and BRISQUE were proved to be improved 1.35 and 1.22 times at ADMIRE 5 compared to ADMIRE 1, respectively. In conclusion, this study was proved that the reconstruction strength of ADMIRE had a great influence on the noise level and overall image quality evaluation of CT images.