  Recently, deep learning-based image denoising methods have achieved promising
performance on test data with the same distribution as training set, where
various denoising models based on synthetic or collected real-world training
data have been learned. However, when handling real-world noisy images, the
denoising performance is still limited. In this paper, we propose a simple yet
effective Bayesian deep ensemble (BDE) method for real-world image denoising,
where several representative deep denoisers pre-trained with various training
data settings can be fused to improve robustness. The foundation of BDE is that
real-world image noises are highly signal-dependent, and heterogeneous noises
in a real-world noisy image can be separately handled by different denoisers.
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet
into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps
to fuse these denoisers. Instead of solely learning pixel-wise weighting maps,
Bayesian deep learning strategy is introduced to predict weighting uncertainty
as well as weighting map, by which prediction variance can be modeled for
improving robustness on real-world noisy images. Extensive experiments have
shown that real-world noises can be better removed by fusing existing denoisers
instead of training a big denoiser with expensive cost. On DND dataset, our BDE
achieves +0.28~dB PSNR gain over the state-of-the-art denoising method.
Moreover, we note that our BDE denoiser based on different Gaussian noise
levels outperforms state-of-the-art CBDNet when applying to real-world noisy
images. Furthermore, our BDE can be extended to other image restoration tasks,
and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets for
image deblurring, image deraining and single image super-resolution,
respectively.
