Problem
Research questions and friction points this paper is trying to address.
CT image conversion
reconstruction kernel
noise reduction
structural preservation
image enhancement
A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion
🤖 AI Summary
This study addresses the inherent trade-off in CT reconstruction between the excessive noise of sharp kernels and the edge blurring of soft kernels by proposing BEMD-QBF, an image enhancement framework that operates without requiring raw projection data. The method innovatively integrates bidimensional empirical mode decomposition (BEMD) with quaternion bilateral filtering (QBF), decomposing images into intrinsic mode functions and applying adaptive filtering within the quaternion domain to achieve synergistic optimization of noise reduction and structural preservation under a unified framework. Experimental results demonstrate that this approach significantly outperforms conventional techniques, including non-local means and anisotropic diffusion, across multiple reconstruction kernels, yielding substantial improvements in SSIM and PSNR metrics as well as overall image structural fidelity.
📝 Abstract
The quality of computed tomography (CT) images is significantly affected by the selection of reconstruction kernels: sharp kernels improve spatial resolution but increase noise, whereas soft kernels diminish noise at the expense of edge clarity. This study presents an innovative enhancement framework utilising Bidimensional Empirical Mode Decomposition in conjunction with Quaternion Bilateral Filtering (BEMD--QBF) to convert sharp-kernel CT images into representations resembling soft-kernels, while maintaining critical anatomical structures. The technique disaggregates each image into intrinsic mode functions via BEMD and analyzes them inside a cohesive quaternion framework to attain efficient noise reduction and structural integrity. The proposed methodology is evaluated using several reconstruction kernels (B50, B46, B41, B36, B35, B31) and compared with recognised filtering strategies, including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering. Quantitative evaluations of the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indicate that BEMD-QBF consistently attains superior structural fidelity and competitive noise reduction across all evaluated kernels. The results underscore the efficacy of the proposed strategy as a viable approach to enhancing post-reconstruction CT images, yielding superior image quality without requiring access to raw projection data.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bidimensional Empirical Mode Decomposition
Quaternion Bilateral Filtering
CT image conversion
Image denoising
Post-reconstruction processing
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