FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise
This addresses the problem of slow inference in blind image denoising for applications like medical imaging or photography, though it is incremental over existing methods.
The paper tackles blind denoising for Poisson-Gaussian noise using only single noisy images, achieving state-of-the-art performance on real-world benchmarks with inference times 10 times faster than prior methods.
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 estimates Poisson-Gaussian noise parameters 2000 times faster than the conventional methods and 2) FBI-Net that realizes a much more efficient BSN for pixelwise affine denoiser in terms of the number of parameters and inference speed. Consequently, we show that our FBI-Denoiser blindly trained solely based on single noisy images can achieve the state-of-the-art performance on several real-world noisy image benchmark datasets with much faster inference time (x 10), compared to BP-AIDE. The official code of our method is available at https://github.com/csm9493/FBI-Denoiser.