Joint-designed achromatic diffractive optics for full-spectrum computational imaging

Abstract

Diffractive optical elements (DOEs) are promising lens candidates in computational imaging because they can drastically reduce the size and weight of image systems. The inherent strong dispersion hinders the direct use of DOEs in full spectrum imaging, causing an unacceptable loss of color fidelity. State-of-the-art methods of designing diffractive achromats either rely on hand-crafted point spread functions (PSFs) as the intermediate metric, or frame a differential end-to-end design pipeline that interprets a 2D lens with limited pixels and only a few wavelengths. In this work, we investigate the joint optimization of achromatic DOE and image processing using a full differentiable optimization model that maps the actual source image to the reconstructed one. This model includes wavelength-dependent propagation block, sensor sampling block, and imaging processing block. We jointly optimize the physical height of DOEs and the parameters of image processing block to minimize the errors over a hyperspectral image dataset. We simplify the rotational symmetric DOE to 1D profle to reduce the computational complexity of 2D propagation. The joint optimization is implemented using auto differentiation of Tensor ow to compute parameter gradients. Simulation results show that the proposed joint design outperforms conventional methods in preserving higher image fidelity.