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 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.
Figure 1 feature-level prior
Figure 2 Image-level prior
3.1AWGN noise Removal
We compare our method with CBM3D and FFDNet. Table 1 reports denoising results on different datasets. We can see that our method can achieve state of the art results and outperform other methods. Moreover, the improvement generalizes well across different datasets as well as different noise levels. In Figure 3, we compare the visual results of different methods for an image in CBSD68 corrupted with noise level σ = 50.world noisy image denoising.
Table 1. Non-blind denoising results of different methods
Figure 3 Denoising comparisons of an image from CBSD68 dataset with noise level σ = 50
3.2 Noise Level Sensitivity
We compare the denoising performances of several different non-blind and blind DIPNet models with different input noise levels. Figure 4 shows the noise level sensitivity curves of different DIPNet models. Specifically, we consider the 5 non-blind DIPNets trained with known noise levels, e.g., “DIPNet-15” represents DIP- Net trained with the fixed noise level σ = 15. We also com- pare the results of DIPNet-BF and DIPNet-BP. It is clear that the non-blind DIPNet-S models with specific noise levels are more sensitive to input noise, especially higher levels. DIPNet-BF demonstrates much stable performance in terms of a wide range of noise levels. DIPNet-BP is more sensitive to higher level noise.
Figure 4 Noise level sensitivity curves of DIPNet models trained with different noise levels.
3.3 Real Noise Removal
Furthermore, we evaluate our blind models on two real noisy image datasets with different methods. Figure 5 shows denoising results on a real noisy image with different method. We can observe that using feature-level prior can effectively improve PSNR but still produce over-smooth textures. The pixel-level prior can help produce sharper appearance but with lower PSNR and SSIM. This is due to the appearance of high-frequency artifacts.
Xianxu Hou, Hongming Luo, Bozhi Liu, Jingxin Liu, Bolei Xu, Ke Sun, Yuanhao Gong, Bozhi Liu, Guoping Qiu. “Learning Deep Image Priors for Image Denoising”. CVPRW 2019.