This work proposes to learn implicit neural representations of shift-variant PSFs for optical imaging systems that use diffractive optical elements. Results show that the proposed method is able to characterize shift-variant PSFs from sparse samples.
@inproceedings{cosi_shiftvariant.svg,bibtex_show={true},equal={true},abbr={Imaging Congress},author={Morales-Norato, David and Arguello, Henry and Rueda-Chac\'{o}n, Hoover},booktitle={Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},journal={Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},keywords={Computational imaging; Diffractive optical elements; Imaging systems; Optical aberration; Optical imaging; Optical systems},pages={CW3B.5},publisher={Optica Publishing Group},title={Shift-variant PSF characterization of DOE-based imaging systems via implicit neural representations},year={2023},url={https://opg.optica.org/abstract.cfm?URI=COSI-2023-CW3B.5},html={https://opg.optica.org/abstract.cfm?URI=COSI-2023-CW3B.5},doi={10.1364/COSI.2023.CW3B.5},preview={cosi_shiftvariant.svg},selected={true}}