Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.
Meanwhile, diverse testing sets are also provided with different types of reflection and scenes.
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #3 on Denoising on Darmstadt Noise Dataset