  Representing visual signals with implicit coordinate-based neural networks,
as an effective replacement of the traditional discrete signal representation,
has gained considerable popularity in computer vision and graphics. In contrast
to existing implicit neural representations which focus on modelling the scene
only, this paper proposes a novel implicit camera model which represents the
physical imaging process of a camera as a deep neural network. We demonstrate
the power of this new implicit camera model on two inverse imaging tasks: i)
generating all-in-focus photos, and ii) HDR imaging. Specifically, we devise an
implicit blur generator and an implicit tone mapper to model the aperture and
exposure of the camera's imaging process, respectively. Our implicit camera
model is jointly learned together with implicit scene models under multi-focus
stack and multi-exposure bracket supervision. We have demonstrated the
effectiveness of our new model on a large number of test images and videos,
producing accurate and visually appealing all-in-focus and high dynamic range
images. In principle, our new implicit neural camera model has the potential to
benefit a wide array of other inverse imaging tasks.
