The Supplementary Materials of the Main Paper: Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training

Comparison between the widely used adversarial training scheme in image restoration and the PNGAN framework. Image degradation and restoration are two important research fields in computational photography. However, too much work is dedicated to handling the image restoration problem and the image degradation remains under-studied. This is the most significant difference between the PNGAN and previous image restoration works. As depicted in Figure 1, we compare the common adversarial training scheme in image restoration and our PNGAN framework.