  We present a controllable camera simulator based on deep neural networks to
synthesize raw image data under different camera settings, including exposure
time, ISO, and aperture. The proposed simulator includes an exposure module
that utilizes the principle of modern lens designs for correcting the luminance
level. It also contains a noise module using the noise level function and an
aperture module with adaptive attention to simulate the side effects on noise
and defocus blur. To facilitate the learning of a simulator model, we collect a
dataset of the 10,000 raw images of 450 scenes with different exposure
settings. Quantitative experiments and qualitative comparisons show that our
approach outperforms relevant baselines in raw data synthesize on multiple
cameras. Furthermore, the camera simulator enables various applications,
including large-aperture enhancement, HDR, auto exposure, and data augmentation
for training local feature detectors. Our work represents the first attempt to
simulate a camera sensor's behavior leveraging both the advantage of
traditional raw sensor features and the power of data-driven deep learning.
