An Attention-Based Denoising Model for Diffusion Weighted Imaging

• URL: http://arxiv.org/abs/2606.03903v1
• Date: Tue, 02 Jun 2026 16:59:09 GMT
• Title: An Attention-Based Denoising Model for Diffusion Weighted Imaging
• Abstract summary: When the scan time is reduced, the image quality often suffers, leading to increased noise in the scans.<n>We propose a noise-aware attention-driven denoising framework that integrates hierarchical Swin Transformer window attention with transformer-based multi-dimensional gated refinement for DWI restoration.<n>Our model achieves a mean PSNR of 33.69dB and SSIM of 0.8539 across noise levels from 1% to 15%, while maintaining stable behavior under severe noise conditions.
• Score: 0.9772661758518332
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Diffusion-weighted imaging (DWI) is used for whole-body cancer screening, but it typically requires a long acquisition time. When the scan time is reduced, the image quality often suffers, leading to increased noise in the scans. Magnitude reconstruction in DWI introduces signal-dependent Rician noise, which makes denoising more challenging for conventional convolution-based methods. To address this limitation, we propose a noise-aware attention-driven denoising framework that integrates hierarchical Swin Transformer window attention with transformer-based multi-dimensional gated refinement for DWI restoration. The model incorporates explicit noise-level conditioning and residual reconstruction to enable adaptive suppression of heteroscedastic noise across a wide range of corruption levels. Experimental evaluation on corrupted DWI scans demonstrates strong restoration performance. Our model achieves a mean PSNR of 33.69~dB and SSIM of 0.8539 across noise levels from 1\% to 15\%, while maintaining stable behavior under severe noise conditions. These results indicate that attention-guided contextual modeling combined with channel-adaptive refinement provides a robust and generalizable solution for DWI denoising.

Related papers

• DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising [10.409009612596714]
  Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD)<n>Short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging.<n>We propose DECADE, an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames.
  arXiv  Detail & Related papers  (2026-03-08T18:31:22Z)
• Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling [19.01755888772832]
  Diffusion magnetic resonance imaging (dMRI) plays a vital role in both clinical diagnostics and neuroscience research.<n>Recent self-supervised and unsupervised denoising methods offer a practical solution by enhancing image quality without requiring clean references.<n>However, most of these methods do not explicitly account for the non-Gaussian noise characteristics commonly present in dMRI magnitude data during the supervised learning process.<n>We introduce noise-corrected training objectives that explicitly model Rician statistics. Specifically, we propose two alternative loss functions: one derived from the first-order moment to remove mean bias, and another from the second-order moment to correct squared-
  arXiv  Detail & Related papers  (2026-02-22T04:32:21Z)
• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT [5.763765207893223]
  GARD (Gamma-based Anatomical Restoration and Denoising) is a novel deep learning approach for OCT image despeckling.<n>GARD employs a Denoising Diffusion Gamma Model to more accurately reflect the statistical properties of speckle.<n>We show GARD significantly outperforms traditional denoising methods and state-of-the-art deep learning models in terms of PSNR, SSIM, and MSE.
  arXiv  Detail & Related papers  (2025-09-12T15:24:41Z)
• Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI [34.361078452552945]
  Real-world MRI acquisitions already contain inherent noise due to thermal fluctuations. We propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation. Our method surpasses the state-of-the-art MRI reconstruction methods, and is highly robust against various noise levels.
  arXiv  Detail & Related papers  (2024-03-08T12:07:18Z)
• Deep Ultrasound Denoising Using Diffusion Probabilistic Models [5.828784149537374]
  Previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture.
  arXiv  Detail & Related papers  (2023-06-12T21:53:32Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [80.20339155618612]
  DiffusionAD is a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network.<n>A rapid one-step denoising paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality.<n>Considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.