End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging

Vincent Sitzmann, Steven Diamond, Yifan Peng, Xiong Dun, Stephen Boyd, Wolfgang Heidrich, Felix Heide, Gordon Wetzstein

Research output: Contribution to journalArticlepeer-review

255 Scopus citations


In typical cameras the optical system is designed first; once it is fixed, the parameters in the image processing algorithm are tuned to get good image reproduction. In contrast to this sequential design approach, we consider joint optimization of an optical system (for example, the physical shape of the lens) together with the parameters of the reconstruction algorithm.We build a fully-differentiable simulation model that maps the true source image to the reconstructed one. The model includes diffractive light propagation, depth and wavelength-dependent effects, noise and nonlinearities, and the image post-processing. We jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images. We implement our joint optimization method using autodifferentiation to efficiently compute parameter gradients in a stochastic optimization algorithm. We demonstrate the efficacy of this approach by applying it to achromatic extended depth of field and snapshot super-resolution imaging.
Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalACM Transactions on Graphics
Issue number4
StatePublished - Jul 31 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to thank Xu Liu and Liang Xu from the State Key Lab of Modern Optical Instrumentation, Zhejiang University, and the KAUST Visual Computing Center for support in designing and prototyping of DOEs. This project was supported by an NSF CAREER award (IIS 1553333), an NSF Graduate Research Fellowship (DGE-114747), a Sloan Fellowship, a Terman Faculty Fellowship, a Stanford Graduate Fellowship, the Intel Compressive Sensing Alliance, and by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant.


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