🤖 AI Summary
Existing medical image denoising methods suffer from artifact generation and loss of anatomical details when applied across heterogeneous modalities (CT/MRI/US) and diverse noise distributions. To address this, we propose a two-stage deep denoising framework: the first stage estimates residual noise via deep residual learning; the second stage introduces a novel self-guided noise-attention mechanism that explicitly models fine-grained correlations between noise characteristics and input features, integrated with cross-modal feature alignment and joint training to achieve unified adaptation across modalities and noise types. Built upon a cascaded architecture, our method consistently outperforms state-of-the-art approaches across multiple quantitative metrics—achieving improvements of +7.64 in PSNR, +0.1021 in SSIM, −0.80 in DeltaE, +0.1855 in VIFP, and −18.54 in MSE—while effectively suppressing artifacts and preserving pathological structures with high fidelity.
📝 Abstract
Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often generate visual artifacts when applied to heterogeneous medical images. This study addresses the limitation of the contemporary denoising methods with an artificial intelligence (AI)-driven two-stage learning strategy. The proposed method learns to estimate the residual noise from the noisy images. Later, it incorporates a novel noise attention mechanism to correlate estimated residual noise with noisy inputs to perform denoising in a course-to-refine manner. This study also proposes to leverage a multimodal learning strategy to generalize the denoising among medical image modalities and multiple noise patterns for widespread applications. The practicability of the proposed method has been evaluated with dense experiments. The experimental results demonstrated that the proposed method achieved state-of-the-art performance by significantly outperforming the existing medical image denoising methods in quantitative and qualitative comparisons. Overall, it illustrates a performance gain of 7.64 in peak signal-to-noise ratio (PSNR), 0.1021 in structural similarity index (SSIM), 0.80 in DeltaE $(Delta E)$ , 0.1855 in visual information fidelity pixelwise (VIFP), and 18.54 in mean squared error (MSE) metrics.