Computed tomography (CT) imaging is essential in medical diagnosis, with image quality heavily dependent on the selection of reconstruction kernels. Sharp kernels improve spatial resolution but increase noise, while soft kernels diminish noise at the expense of edge definition. This paper introduces a unique Variational Mode Decomposition with Quaternion Bilateral Filtering (VMD-QBF) method to convert sharp-kernel CT images into soft-kernel versions while maintaining critical structural information. The suggested method is assessed in comparison to conventional denoising techniques, such as Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF), utilizing various reconstruction kernels (B50, B46, B41, B36, B35, B31). The evaluation is performed with Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Experimental findings indicate that VMD-QBF surpasses traditional filtering methods, attaining minimal MSE and maximal PSNR, while preserving enhanced structural similarity across all evaluated kernels. The results validate the efficacy of the suggested strategy in reducing noise while maintaining essential image characteristics, positioning it as a viable solution for post-reconstruction CT image enhancement.