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A hybrid imaging system is a simultaneous physical arrangement of arefractive lens and a multilevel phase mask (MPM) as a diffractive opticalelement (DOE). The favorable properties of the hybrid setup are improvedextended-depth-of-field (EDoF) imaging and low chromatic aberrations. We builta fully differentiable image formation model in order to use neural networktechniques to optimize imaging. At the first stage, the design framework relieson the model-based approach with numerical simulation and end-to-end jointoptimization of both MPM and imaging algorithms. In the second stage, MPM isfixed as found at the first stage, and the image processing is optimizedexperimentally using the CNN learning-based approach with MPM implemented by aspatial light modulator. The paper is concentrated on a comparative analysis ofimaging accuracy and quality for design with various basic optical parameters:aperture size, lens focal length, and distance between MPM and sensor. We pointout that the varying aperture size, lens focal length, and distance between MPMand sensor are for the first time considered for end-to-end optimization ofEDoF. We numerically and experimentally compare the designs for visiblewavelength interval [400-700]nm and the following EDoF ranges: [0.5-100]m forsimulations and [0.5-1.9]m for experimental tests. This study concerns anapplication of hybrid optics for compact cameras with aperture [5-9] mm anddistance between MPM and sensor [3-10]mm.

On design of hybrid diffractive optics for achromatic extended depth-of-field (EDoF) RGB imaging