Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising

fahad shamshad, Muhammad Awais, Muhammad Asim, Ali Ahmed

Abstract: 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 the 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 the SURE based approach for denoising X-ray images.