  Image denoising is the process of removing noise from noisy images, which is
an image domain transferring task, i.e., from a single or several noise level
domains to a photo-realistic domain. In this paper, we propose an effective
image denoising method by learning two image priors from the perspective of
domain alignment. We tackle the domain alignment on two levels. 1) the
feature-level prior is to learn domain-invariant features for corrupted images
with different level noise; 2) the pixel-level prior is used to push the
denoised images to the natural image manifold. The two image priors are based
on $\mathcal{H}$-divergence theory and implemented by learning classifiers in
adversarial training manners. We evaluate our approach on multiple datasets.
The results demonstrate the effectiveness of our approach for robust image
denoising on both synthetic and real-world noisy images. Furthermore, we show
that the feature-level prior is capable of alleviating the discrepancy between
different level noise. It can be used to improve the blind denoising
performance in terms of distortion measures (PSNR and SSIM), while pixel-level
prior can effectively improve the perceptual quality to ensure the realistic
outputs, which is further validated by subjective evaluation.
