English(EN) Decomposition-Guided Curvelet Thresholding for Sharp-to-Soft CT Kernel Conversion

新的CT图像去噪框架结合了分解和小波变换阈值处理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种新颖的CT图像混合去噪框架，该框架结合了多种分解技术（如EMD、VMD、MEMD和BEMD）与小波变换阈值处理。该方法对每个分解模式分别使用软阈值和硬阈值处理，然后将它们重新组合以重建最终图像。在具有B50、B46、B41和B36等各种核的标准CT数据集上进行的评估表明，去噪效果显著提高，其中VMD始终产生最高的PSNR和SSIM分数。该研究还分析了软阈值和硬阈值之间的权衡，指出软阈值保留了复杂的细节，而硬阈值提供了卓越的降噪效果。 AI

排序理由该项目是一篇学术论文，详细介绍了一种新的图像去噪方法。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.CV TIER_1 English(EN) · Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski, Adam Piorkowski · 2026-10-07 04:00

用于软化CT核的分解引导曲线变换阈值处理

arXiv:2610.07067v1 Announce Type: new Abstract: Image denoising is a crucial task in image processing, focused on improving image quality by minimizing noise while maintaining essential structural elements. This study presents a hybrid denoising framework that combines several de…

本文如何被排名

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用于软化CT核的分解引导曲线变换阈值处理

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