Fully Convolutional Pixel Adaptive Image Denoiser
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We propose a new denoising algorithm, dubbed as Fully Convolutional Adaptive Image DEnoiser (FC-AIDE), that can learn from offline supervised training set with a fully convolutional neural network architecture as well as adaptively fine-tune the denoiser for each given noisy image. We mainly follow the framework of the recently proposed Neural AIDE, which formulates the denoiser to be context-based pixelwise affine mappings and utilizes the unbiased estimator of MSE of such denoisers. The three main contributions we make to significantly improve upon the original Neural AIDE are the followings; 1) implementing a novel fully convolutional architecture that boosts the base supervised model, 2) introducing data augmentation for adaptive fine-tuning to achieve much stronger adaptivity, and 3) proposing an effective unknown noise level estimation method. As a result, FC-AIDE is shown to significantly outperform the state-of-the-art CNN-based denoisers on two standard benchmark dataset as well as on a much challenging blind denoising dataset, in which nothing is known about the noise level, noise distribution, or image characteristics.
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