  Deep optical optimization has recently emerged as a new paradigm for
designing computational imaging systems using only the output image as the
objective. However, it has been limited to either simple optical systems
consisting of a single element such as a diffractive optical element (DOE) or
metalens, or the fine-tuning of compound lenses from good initial designs. Here
we present a DeepLens design method based on curriculum learning, which is able
to learn optical designs of compound lenses ab initio from randomly initialized
surfaces without human intervention, therefore overcoming the need for a good
initial design. We demonstrate the effectiveness of our approach by fully
automatically designing both classical imaging lenses and a large field-of-view
extended depth-of-field computational lens in a cellphone-style form factor,
with highly aspheric surfaces and a short back focal length.
