TL;DR
FBI-Denoiser introduces a fast, blind image denoising method for Poisson-Gaussian noise that significantly improves inference speed while maintaining state-of-the-art performance using only single noisy images for training.
Contribution
The paper proposes FBI-Denoiser, combining PGE-Net and FBI-Net to achieve faster noise parameter estimation and denoising, outperforming existing methods in speed and accuracy.
Findings
Achieves 10x faster inference than BP-AIDE
Maintains state-of-the-art denoising performance
Effective with only single noisy images for training
Abstract
We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which no additional information about clean images or noise level parameters is available. Particularly, when only "single" noisy images are available for training a denoiser, the denoising performance of existing methods was not satisfactory. Recently, the blind pixelwise affine image denoiser (BP-AIDE) was proposed and significantly improved the performance in the above setting, to the extent that it is competitive with denoisers which utilized additional information. However, BP-AIDE seriously suffered from slow inference time due to the inefficiency of noise level estimation procedure and that of the blind-spot network (BSN) architecture it used. To that end, we propose Fast Blind Image Denoiser (FBI-Denoiser) for Poisson-Gaussian noise, which consists of two neural network models; 1) PGE-Net that…
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