Neural Adaptive Image Denoiser

Sungmin Cha, Taesup Moon

Abstract: 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.