Training deep learning based denoisers without ground truth data

Recent deep learning based denoisers often outperform state-of-the-art conventional denoisers such as BM3D. They are typically trained to minimize the mean squared error (MSE) between the output of a deep neural network and the ground truth image. In deep learning based denoisers, it is important to use high quality noiseless ground truth for high performance, but it is often challenging or even infeasible to obtain such a clean image in application areas such as hyperspectral remote sensing and medical imaging. We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep neural network denoisers only with noisy images. We demonstrated that our SURE based method without ground truth was able to train deep neural network denoisers to yield performance close to deep learning denoisers trained with ground truth and to outperform state-of-the-art BM3D. Further improvements were achieved by including noisy test images for training denoiser networks using our proposed SURE based method.