Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images

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 multi-modal 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 Pixel-wise (VIFP), and 18.54 in Mean Squared Error (MSE) metrics.

IEEE Transactions on Radiation and Plasma Medical Sciences (2024)

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• Medical image denoising using convolutional denoising autoencoders
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
• Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
• DarkDeblur: Learning single-shot image deblurring in low-light condition
• Adversarial Distortion Learning for Medical Image Denoising
• SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction
• FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer