  Conventional image reconstruction models for lensless cameras often assume
that each measurement results from convolving a given scene with a single
experimentally measured point-spread function. These image reconstruction
models fall short in simulating lensless cameras truthfully as these models are
not sophisticated enough to account for optical aberrations or scenes with
depth variations. Our work shows that learning a supervised primal-dual
reconstruction method results in image quality matching state of the art in the
literature without demanding a large network capacity. This improvement stems
from our primary finding that embedding learnable forward and adjoint models in
a learned primal-dual optimization framework can even improve the quality of
reconstructed images (+5dB PSNR) compared to works that do not correct for the
model error. In addition, we built a proof-of-concept lensless camera prototype
that uses a pseudo-random phase mask to demonstrate our point. Finally, we
share the extensive evaluation of our learned model based on an open dataset
and a dataset from our proof-of-concept lensless camera prototype.
