A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion

A study posted to arXiv (2610.07071v1) presents BEMD-QBF, a framework combining Bidimensional Empirical Mode Decomposition with Quaternion Bilateral Filtering that converts sharp-kernel CT images into soft-kernel-like representations while preserving anatomical structures. Tested across reconstruction kernels B50, B46, B41, B36, B35 and B31 against Non-Local Means, Anisotropic Diffusion, Bilateral Filtering and Quaternion Bilateral Filtering, the method achieved superior Structural Similarity Index (SSIM) and competitive Peak Signal-to-Noise Ratio (PSNR) results without requiring access to raw projection data.

arXiv:2610.07071v1 Announce Type: new 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.