Super-resolution image projection over an extended depth of field using a diffractive decoder

Hanlong Chen; Çağatay Işıl; Che-Yung Shen; Shiqi Chen; Tianyi Gan; Mona Jarrahi; Aydogan Ozcan

doi:10.1038/s41377-026-02320-7

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Super-resolution image projection over an extended depth of field using a diffractive decoder

Original Articles

- Super-resolution image projection over an extended depth of field using a diffractive decoder
- Light: Science & Applications Vol. 15, Issue 8, Pages: 2667-2683(2026)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
  2.Bioengineering Department, University of California, Los Angeles, CA, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-026-02320-7
  CLC：
- Received：05 October 2025，
  
  Revised：2026-04-11，
  
  Accepted：15 April 2026，
  
  Online First：18 May 2026，
  
  Published：31 August 2026
- Accepted：
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Chen, H. L. et al. Super-resolution image projection over an extended depth of field using a diffractive decoder. Light: Science & Applications, 15, 2667-2683 (2026).
DOI：

Chen, H. L. et al. Super-resolution image projection over an extended depth of field using a diffractive decoder. Light: Science & Applications, 15, 2667-2683 (2026). DOI： 10.1038/s41377-026-02320-7.

摘要

Abstract

Image projection systems must be efficient in data storage

computation and transmission while maintaining a large space-bandwidth-product (SBP) at their output. Here

we introduce a hybrid image projection system that achieves extended depth-of-field (DOF) with improved resolution

combining a convolutional neural network (CNN)-based digital encoder with an all-optical diffractive decoder. A CNN-based encoder compresses input images into compact phase representations

which are subsequently displayed by a low-resolution (LR) projector and processed by an analog diffractive decoder for all-optical image reconstruction. This optical decoder is completely passive

designed to synthesize pixel super-resolved image projections that feature an extended DOF while eliminating the need for additional power consumption for super-resolved image reconstruction. Our pixel super-resolution (PSR) image projection system demonstrates high-fidelity image synthesis over an extended DOF of ≥250

where

is the illumination wavelength

concurrently offering up to ~16-fold SBP improvement at each lateral plane. The feasibility of this approach is validated through proof-of-concept experiments conducted in both the terahertz and visible spectral regions

demonstrating the scalability of our hybrid system across different wavelengths. This image projection architecture can reduce data storage and transmission requirements for display systems without imposing additional power constraints on the optical decoder. Beyond extended DOF PSR image projection

the underlying principles of this approach can be extended to various applications

including optical metrology and microscopy.

关键词

Keywords

references

Xiong, J. H. et al. Augmented reality and virtual reality displays: emerging technologies and future perspectives. Light Sci. Appl. 10 , 216 (2021)..

Venkatesan, M. et al. Virtual and augmented reality for biomedical applications. Cell Rep. Med. 2 , 100348 (2021)..

Kooi, F. L. & Toet, A. Visual comfort of binocular and 3D displays. Displays 25 , 99–108 (2004)..

Blanche, P. A. Holography, and the future of 3D display. Light Adv. Manuf. 2 , 28 (2021)..

Maimone, A., Georgiou, A. & Kollin, J. S. Holographic near-eye displays for virtual and augmented reality. ACM Trans. Graph. 36 , 85 (2017)..

Yaraş, F., Kang, H. & Onural, L. State of the art in holographic displays: a survey. J. Disp. Technol. 6 , 443–454 (2010)..

Chang, C. L. et al. Toward the next-generation VR/AR optics: a review of holographic near-eye displays from a human-centric perspective. Optica 7 , 1563–1578 (2020)..

Blinder, D. et al. The state-of-the-art in computer generated holography for 3D display. Light Adv. Manuf. 3 , 35 (2022)..

Park, J. H. & Lee, B. Holographic techniques for augmented reality and virtual reality near-eye displays. Light Adv. Manuf. 3 , 9 (2022)..

Gilles, A. & Gioia, P. Real-time layer-based computer-generated hologram calculation for the Fourier transform optical system. Appl. Opt. 57 , 8508–8517 (2018)..

Blinder, D., Nishitsuji, T. & Schelkens, P. Real-time computation of 3D wireframes in computer-generated holography. IEEE Trans. Image Process. 30 , 9418–9428 (2021)..

Horisaki, R., Takagi, R. & Tanida, J. Deep-learning-generated holography. Appl. Opt. 57 , 3859–3863 (2018)..

Chakravarthula, P. et al. Learned hardware-in-the-loop phase retrieval for holographic near -eye displays. ACM Trans. Graph. 39 , 186 (2020)..

Peng, Y. F. et al. Neural holography with camera-in-the-loop training. ACM Trans. Graph. 39 , 185 (2020)..

Shi, L. et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature 591 , 234–239 (2021)..

Peng, Y. F. et al. Speckle-free holography with partially coherent light sources and camera-in-the-loop calibration. Sci. Adv. 7 , eabg5040 (2021)..

Wu, J. C. et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt. Lett. 46 , 2908–2911 (2021)..

Situ, G. H. Deep holography. Light Adv. Manuf. 3 , 13 (2022)..

Shi, L., Li, B. C. & Matusik, W. End-to-end learning of 3D phase-only holograms for holographic display. Light Sci. Appl. 11 , 247 (2022)..

Choi, S. et al. Time-multiplexed neural holography: a flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators. ACM SIGGRAPH 2022 Conference Proceedings. Vancouver, BC, Canada: Association for Computing Machinery, 2022, 32.

Gopakumar, M. et al. Full-colour 3D holographic augmented-reality displays with metasurface waveguides. Nature 629 , 791–797 (2024)..

Işıl, Ç. et al. Super-resolution image display using diffractive decoders. Sci. Adv. 8 , eadd3433 (2022)..

Tseng, E. et al. Neural étendue expander for ultra-wide-angle high-fidelity holographic display. Nat. Commun. 15 , 2907 (2024)..

Ulyanov, D., Vedaldi, A. & Lempitsky, V. Instance normalization: the missing ingredient for fast stylization. Print at https://doi.org/10.48550/arXiv.1607.08022 https://doi.org/10.48550/arXiv.1607.08022 (2016).

He, K. M. et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015 IEEE International Conference on Computer Vision (ICCV). 1026-1034 (IEEE, 2015).

Cohen, G. et al. EMNIST: an extension of MNIST to handwritten letters. Print at https://doi.org/10.48550/arXiv.1702.05373 https://doi.org/10.48550/arXiv.1702.05373 (2017).

Greenbaum, A. et al. Increased space-bandwidth product in pixel super-resolved lensfree on-chip microscopy. Sci. Rep. 3 , 1717 (2013)..

Goodman, J. W. Introduction to Fourier Optics. (McGraw-Hill Book Co., 1968).

Dowski, E. R. & Cathey, W. T. Extended depth of field through wave-front coding. Appl. Opt. 34 , 1859–1866 (1995)..

Rai, M. R. & Rosen, J. Depth-of-field engineering in coded aperture imaging. Opt. Express 29 , 1634–1648 (2021)..

Akpinar, U. et al. Learning wavefront coding for extended depth of field imaging. IEEE Trans. Image Process. 30 , 3307–3320 (2021)..

Xu, Z. Q. et al. DSCCNet for high-quality 4K computer-generated holograms. Opt. Express 33 , 13733–13747 (2025)..

Liu, N. H. et al. Propagation-adaptive 4K computer-generated holography using physics-constrained spatial and Fourier neural operator. Nat. Commun. 16 , 7761 (2025)..

Mengu, D. et al. Misalignment resilient diffractive optical networks. Nanophotonics 9 , 4207–4219 (2020)..

Mengu, D., Rivenson, Y. & Ozcan, A. Scale-, shift-, and rotation-invariant diffractive optical networks. ACS Photonics 8 , 324–334 (2021)..

Zuo, C. et al. Deep learning in optical metrology: a review. Light Sci. Appl. 11 , 39 (2022)..

Goi, E. et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl. 10 , 40 (2021)..

Chen, H. et al. Diffractive deep neural networks at visible wavelengths. Engineering 7 , 1483–1491 (2021)..

Bai, B. J. et al. Data-class-specific all-optical transformations and encryption. Adv. Mater. 35 , 2212091 (2023)..

Shen, C. Y. et al. Broadband unidirectiona l visible imaging using wafer-scale nano-fabrication of multi-layer diffractive optical processors. Light Sci. Appl. 14 , 267 (2025)..

Mengu, D. et al. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top. Quantum Electron. 26 , 3700114 (2020)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Chen, S. Q. et al. Optical generative models. Nature 644 , 903–911 (2025)..

Ha, D.&Eck, D. A neural representation of sketch drawings. Proceedings of the 6th International Conference on Learning Representations. OpenReview. net: Vancouver, BC, Canada, 2018.

Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323 , 533–536 (1986)..

Wang, Z. et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 , 600–612 (2004)..

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