  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.
