Title: Training deep learning based denoisers without ground truth data,
Abstract: 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.