发表机构
AGH University of Krakow; Sano Centre for Computational Medicine; Jagiellonian University; Wroclaw University of Science and Technology（克拉科夫AGH科技大学; 萨诺计算医学中心; 雅盖隆大学; 弗罗茨瓦夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合多种分解技术与曲线波阈值处理的混合去噪框架，用于CT图像去噪，其中VMD方法在PSNR和SSIM上表现最佳，优于传统方法。
AI 中文摘要
图像去噪是图像处理中的一项关键任务，旨在通过最小化噪声同时保持必要的结构元素来提高图像质量。本研究提出了一种混合去噪框架，该框架结合了多种分解技术，包括经验模态分解（EMD）、变分模态分解（VMD）、多通道EMD（MEMD）和二维EMD（BEMD），并与曲线波变换阈值处理相结合。每个分解模态都经过软阈值和硬阈值处理，然后将去噪后的模态组合以重建最终图像。对使用不同核（B50、B46、B41、B36）重建的标准CT图像数据集的全面评估显示，去噪效果显著增强。VMD始终达到最高的峰值信噪比（PSNR）和结构相似性指数（SSIM），表明其具有卓越的噪声抑制和特征保留能力。研究分析了软阈值和硬阈值之间的权衡：软阈值保留复杂的视觉细节，而硬阈值提供更强的噪声抑制。所提出的方法在参考和非参考质量指标上均优于传统技术，表明其在医学成像中具有更广泛应用的潜力，并可与自适应阈值算法相结合。
英文摘要
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 decomposition techniques, including empirical mode decomposition (EMD), variational mode decomposition (VMD), multichannel EMD (MEMD), and bidimensional EMD (BEMD), with curvelet transform thresholding. Each decomposition mode undergoes processing through both soft and hard thresholding, and the denoised modes are combined to rebuild the final image. Comprehensive evaluations of standard CT image datasets reconstructed with various kernels (B50, B46, B41, B36) reveal substantial enhancements in denoising efficacy. VMD consistently achieves the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), signifying exceptional noise reduction and feature preservation. The study analyses the trade-offs between soft and hard thresholding: soft thresholding maintains intricate visual details, whilst harsh thresholding provides enhanced noise reduction. The suggested method surpasses traditional techniques in both reference and non-reference quality criteria, indicating its potential for broader application in medical imaging and future incorporation with adaptive thresholding algorithms.
DOI：10.1007/978-3-032-37933-7_39
