  Noise synthesis is a challenging low-level vision task aiming to generate
realistic noise given a clean image along with the camera settings. To this
end, we propose an effective generative model which utilizes clean features as
guidance followed by noise injections into the network. Specifically, our
generator follows a UNet-like structure with skip connections but without
downsampling and upsampling layers. Firstly, we extract deep features from a
clean image as the guidance and concatenate a Gaussian noise map to the
transition point between the encoder and decoder as the noise source. Secondly,
we propose noise synthesis blocks in the decoder in each of which we inject
Gaussian noise to model the noise characteristics. Thirdly, we propose to
utilize an additional Style Loss and demonstrate that this allows better noise
characteristics supervision in the generator. Through a number of new
experiments, we evaluate the temporal variance and the spatial correlation of
the generated noise which we hope can provide meaningful insights for future
works. Finally, we show that our proposed approach outperforms existing methods
for synthesizing camera noise.
