Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising
This addresses the challenge of obtaining clean training data in medical imaging, though it appears incremental as it applies an existing SURE method to X-ray denoising.
The paper tackled the problem of denoising X-ray images without needing noiseless ground truth data by using Stein's Unbiased Risk Estimator to train a deep convolutional neural network, and demonstrated its effectiveness in experiments.
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in many medical imaging applications such as X-rays. To circumvent this issue, we leverage recently proposed approach of [7] that incorporates Stein's Unbiased Risk Estimator (SURE) to train a deep convolutional neural network without requiring denoised ground truth X-ray data. Our experimental results demonstrate the effectiveness of SURE based approach for denoising X-ray images.