DergiPark Akademik
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Changing occupational structure and sectoral labour productivity differentials in Japan’s economic growth, 1874-2010
No full textDoes the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability
No full textLearning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks
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Tackling illegitimate intertextuality through socialization - An action research project
No full textA Study on the Relationship between Industrial Property Law and Administrative Law
No full text日本学術振興会21K13188機能不全の解消に向けた行政罰各論の領域横断的研究 ―主要６分野の比較分析―本稿は，JSPS 科研費「機能不全の解消に向け た行政罰各論の領域横断的研究」 （課題番号 21K13188）による研究成果の一部である
Spectral X-ray CT Image Denoising using Weighted Local Regression and Noise-Insensitive Feature Dimensionality Reduction
No full textThis study introduces a novel denoising method for spectral X-ray computed tomography (CT) images using weighted local regression (WLR). The proposed method exploits the common structural information present across different energy bins. Denoised pixel intensities of a certain energy bin are estimated using the intensities of the other energy bins via WLR. Denoising is achieved by applying a WLR model to the noisy pixel intensities of all energy bins, excluding the target bin, which obtains approximate noise-free intensities for the target energy bin. The performance of our approach was assessed using synthetic spectral X-ray CT images produced using a Monte Carlo photon simulator called the Electron Gamma Shower 5 (EGS5). Both qualitative and quantitative evaluations demonstrated that our approach effectively reduced noise across all energy bins while maintaining image sharpness. Comparisons with common denoising methods demonstrate the effectiveness of the proposed method
