  A hybrid imaging system is a simultaneous physical arrangement of a
refractive lens and a multilevel phase mask (MPM) as a diffractive optical
element (DOE). The favorable properties of the hybrid setup are improved
extended-depth-of-field (EDoF) imaging and low chromatic aberrations. We built
a fully differentiable image formation model in order to use neural network
techniques to optimize imaging. At the first stage, the design framework relies
on the model-based approach with numerical simulation and end-to-end joint
optimization of both MPM and imaging algorithms. In the second stage, MPM is
fixed as found at the first stage, and the image processing is optimized
experimentally using the CNN learning-based approach with MPM implemented by a
spatial light modulator. The paper is concentrated on a comparative analysis of
imaging accuracy and quality for design with various basic optical parameters:
aperture size, lens focal length, and distance between MPM and sensor. We point
out that the varying aperture size, lens focal length, and distance between MPM
and sensor are for the first time considered for end-to-end optimization of
EDoF. We numerically and experimentally compare the designs for visible
wavelength interval [400-700]nm and the following EDoF ranges: [0.5-100]m for
simulations and [0.5-1.9]m for experimental tests. This study concerns an
application of hybrid optics for compact cameras with aperture [5-9] mm and
distance between MPM and sensor [3-10]mm.
