The image formation model and any image priors and regular terms are expressed as a single objective function, which is solved using a proximal
operator framework.
Proximal operators decouple the individual terms in the objective
function in a principled way, making it possible to separately implement
the operators for the data term and each regular.
This approach enables mixing and matching different, highly optimized
implementations of data terms and regular.
Our framework therefore achieves both the improved image quality of a fully integrated optimization and the separation of concerns that gave rise to
the traditional pipeline approach.
We detail and analyze our design choices, and conclude that a single
specific set of priors can be used for a variety of applications, and still
outperforms other state-of-the-art methods.
While we demonstrate significant improvements in quality and simplicity for traditional camera designs, we believe that our approach will achieve its full
potential with future computational cameras that have significantly
more complex image formation models.