Training Deep Learning based Denoisers without Ground Truth Data
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Recent deep learning based denoisers are trained to minimize the mean squared error (MSE) between the output of a network and the ground truth noiseless image in the training data. Thus, it is crucial to have high quality noiseless training data for high performance denoisers. Unfortunately, in some application areas such as medical imaging, it is expensive or even infeasible to acquire such a clean ground truth image. We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep learning based denoisers without ground truth data. We demonstrated that our SURE based method only with noisy input data was able to train CNN based denoising networks that yielded performance close to that of the original MSE based deep learning denoisers with ground truth data.
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