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New CT Image Denoising Framework Combines Decomposition and Curvelet Thresholding

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Researchers have developed a novel hybrid denoising framework for CT images that combines multiple decomposition techniques like EMD, VMD, MEMD, and BEMD with curvelet transform thresholding. This method processes each decomposition mode using both soft and hard thresholding before recombining them to reconstruct the final image. Evaluations on standard CT datasets with various kernels, such as B50, B46, B41, and B36, demonstrated significant improvements in denoising effectiveness, with VMD consistently yielding the highest PSNR and SSIM scores. The study also analyzed the trade-offs between soft and hard thresholding, noting that soft thresholding preserves intricate details while hard thresholding offers superior noise reduction. AI

RANK_REASON The item is an academic paper detailing a new method for image denoising. [lever_c_demoted from research: ic=1 ai=0.4]

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New CT Image Denoising Framework Combines Decomposition and Curvelet Thresholding

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The item is an academic paper detailing a new method for image denoising. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski, Adam Piorkowski ·

    Decomposition-Guided Curvelet Thresholding for Sharp-to-Soft CT Kernel Conversion

    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…

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