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[2602.22544] HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
Summary
HARU-Net introduces a novel deep learning architecture for denoising cone-beam computed tomography (CBCT) images, enhancing edge preservation and diagnostic quality.
Why It Matters
This research addresses a critical challenge in medical imaging, where low-dose CBCT scans often suffer from noise that obscures important anatomical details. By improving denoising methods, HARU-Net enhances diagnostic accuracy and patient care in dental and maxillofacial imaging.
Key Takeaways
- HARU-Net integrates hybrid attention mechanisms for improved feature extraction.
- Achieves superior performance in denoising compared to state-of-the-art methods.
- Demonstrates significant computational efficiency, making it practical for clinical use.
- Utilizes a unique cadaver dataset for training, addressing data scarcity in CBCT.
- Enhances visibility of soft tissues and fine anatomical structures in low-dose imaging.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2602.22544 (eess) [Submitted on 26 Feb 2026] Title:HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography Authors:Khuram Naveed, Ruben Pauwels View a PDF of the paper titled HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography, by Khuram Naveed and Ruben Pauwels View PDF HTML (experimental) Abstract:Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spatially varying noise that degrades soft-tissue visibility and obscures fine anatomical structures. Classical denoising methods struggle to suppress noise in CBCT while preserving edges. Although deep learning-based approaches offer high-fidelity restoration, their use in CBCT denoising is limited by the scarcity of high-resolution CBCT data for supervised training. To address this research gap, we propose a novel Hybrid Attention Residual U-Net (HARU-Net) for high-quality denoising of CBCT data, trained on a cadaver dataset of human hemimandibles acquired using a high-resolution protocol of the 3D Accuitomo 170 (J. Morita, Kyoto, Japan) CBCT system. The novel contribution of this approach is the integration of three complementary architectural components: (i) a hybrid attention transformer block (HAB) embedded within each skip connection to selectively emphasize salie...