The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging
Abstract
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.
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Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light
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Image Super-Resolution Using Deep Convolutional Networks
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks
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ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Accelerating the Super-Resolution Convolutional Neural Network
Dong, C., Loy, C. C., & Tang, X. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_25 · Cited by 3,786
Single image super-resolution from transformed self-exemplars
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Deeply-Recursive Convolutional Network for Image Super-Resolution
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Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
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Inverse design enables large-scale high-performance meta-optics reshaping virtual reality
Li, Z., Pestourie, R., Park, J.-S., Huang, Y.-W., Johnson, S. G., & Capasso, F. (2022) · Nature Communications · doi:10.1038/s41467-022-29973-3 · Cited by 280
Learning Privacy-preserving Optics for Human Pose Estimation
Hinojosa, C., Niebles, J. C., & Arguello, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00257 · Cited by 62
What if eye...? Computationally recreating vision evolution
Tiwary, K., Young, A., Tasneem, Z., Klinghoffer, T., Dave, A., Poggio, T., Nilsson, D.-E., Cheung, B., & Raskar, R. (2025) · Science Advances · doi:10.1126/sciadv.ady2888 · Cited by 10
“The optical element is represented as a 2D array that can be programmed into lenses of different shapes [modeled as a diffractive optical element (DOE)] ( 61 – 63 ).”
Metadata
- Authors
- Sitzmann, V., Diamond, S., Peng, Y., Dun, X., Boyd, S., Heidrich, W., Heide, F., & Wetzstein, G.
- Venue
- ACM Transactions on Graphics
- Published
- 2018-07-30
- Volume
- 37
- Issue
- 4
- Pages
- 1-13
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2809900749
- Cited by
- 478
- References
- 49
- FWCI
- 29.33
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sitzmann, V., Diamond, S., Peng, Y., Dun, X., Boyd, S., Heidrich, W., Heide, F., & Wetzstein, G. (2018). End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Transactions on Graphics. https://doi.org/10.1145/3197517.3201333
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