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NEURAL ADAPTIVE IMAGE DENOISER

Citation Author(s):
Sungmin Cha, Taesup Moon
Submitted by:
Sungmin Cha
Last updated:
14 April 2018 - 8:37am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Sungmin Cha
Paper Code:
3035
 

We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context- based affine mappings that get applied to each noisy pixel. Our formulation enables using SURE (Stein’s Unbiased Risk Estimator)-like estimated losses of those mappings as empirical risks to minimize. In results, we can combine both supervised training of the network parameters from a separate dataset and adaptive fine-tuning of them using the given noisy image subject to denoising. Our algorithm with a plain fully connected architecture is shown to attain a competitive denoising performance on benchmark datasets compared to the strong baselines. Furthermore, Neural AIDE can robustly correct the mismatched noise level in the supervised learning via fine-tuning, of which adaptivity is absent in other neural network-based denoisers.

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