Focal Sweep Imaging with
Multi-focal Diffractive Optics

Yifan (Evan) Peng, Xiong Dun, Qilin Sun, Felix Heide, Wolfgang Heidrich
A project collaborated with UBC, accepted to IEEE International Conference on Computational Photography 2018

Cropped regions of real world results. Top: degraded inputs; Bottom: reconstruction results using deconvolution with TV and cross-channel regularizations. For experimental convenience, we capture a depth range from 1.5m to 3.5m for the left two scenes and from 2m to 8m for the right scene with a sweep distance of 0.5mm.


Depth-dependent defocus results in a limited depth-of-field in consumer-level cameras. Computational imaging provides alternative solutions to resolve all-in-focus images with the assistance of designed optics and algorithms. In this work, we extend the concept of focal sweep from refractive optics to diffractive optics, where we fuse multiple focal powers onto one single element. In contrast to state-of-the-art sweep models, ours can generate better-conditioned point spread function (PSF) distributions along the expected depth range with drastically shortened (40%) sweep distance. Further by encoding axially asymmetric PSFs subject to color channels, and then sharing sharp information across channels, we preserve details as well as color fidelity. We prototype two diffractive imaging systems that work in the monochromatic and RGB color domain. Experimental results indicate that the depth-of-field can be significantly extended with fewer artifacts remaining after the deconvolution.


Paper: [Peng2018FocalSweep.pdf (~2.2MB)] 

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