At the pinnacle of computational imaging is the co-optimization of camera and
algorithm. This, however, is not the only form of computational imaging. In
problems such as imaging through adverse weather, the bigger challenge is how
to accurately simulate the forward degradation process so that we can
synthesize data to train reconstruction models and/or integrating the forward
model as part of the reconstruction algorithm. This article introduces the
concept of computational image formation (CIF). Compared to the standard
inverse problems where the goal is to recover the latent image x
from the observation y=G(x), CIF shifts the
focus to designing an approximate mapping Hθ​ such that
Hθ​≈G while giving a better image
reconstruction result. The word ``computational'' highlights the fact that the
image formation is now replaced by a numerical simulator. While matching nature
remains an important goal, CIF pays even greater attention on strategically
choosing an Hθ​ so that the reconstruction performance is
maximized.
The goal of this article is to conceptualize the idea of CIF by elaborating
on its meaning and implications. The first part of the article is a discussion
on the four attributes of a CIF simulator: accurate enough to mimic
G, fast enough to be integrated as part of the reconstruction,
providing a well-posed inverse problem when plugged into the reconstruction,
and differentiable in the backpropagation sense. The second part of the article
is a detailed case study based on imaging through atmospheric turbulence. The
third part of the article is a collection of other examples that fall into the
category of CIF. Finally, thoughts about the future direction and
recommendations to the community are shared