“The diffractive achromat full spectrum computational imaging with diffractive optics”

  • ©Yifan (Evan) Peng, Qiang Fu, Felix Heide, and Wolfgang Heidrich

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Title:

    The diffractive achromat full spectrum computational imaging with diffractive optics

Session/Category Title: COMPUTATIONAL CAMERAS


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Abstract:


    Diffractive optical elements (DOEs) have recently drawn great attention in computational imaging because they can drastically reduce the size and weight of imaging devices compared to their refractive counterparts. However, the inherent strong dispersion is a tremendous obstacle that limits the use of DOEs in full spectrum imaging, causing unacceptable loss of color fidelity in the images. In particular, metamerism introduces a data dependency in the image blur, which has been neglected in computational imaging methods so far. We introduce both a diffractive achromat based on computational optimization, as well as a corresponding algorithm for correction of residual aberrations. Using this approach, we demonstrate high fidelity color diffractive-only imaging over the full visible spectrum. In the optical design, the height profile of a diffractive lens is optimized to balance the focusing contributions of different wavelengths for a specific focal length. The spectral point spread functions (PSFs) become nearly identical to each other, creating approximately spectrally invariant blur kernels. This property guarantees good color preservation in the captured image and facilitates the correction of residual aberrations in our fast two-step deconvolution without additional color priors. We demonstrate our design of diffractive achromat on a 0.5mm ultrathin substrate by photolithography techniques. Experimental results show that our achromatic diffractive lens produces high color fidelity and better image quality in the full visible spectrum.

References:


    1. Aieta, F., Genevet, P., Kats, M. A., Yu, N., Blanchard, R., Gaburro, Z., and Capasso, F. 2012. Aberration-free ultrathin flat lenses and axicons at telecom wavelengths based on plasmonic metasurfaces. Nano letters 12, 9, 4932–4936.Google Scholar
    2. Aieta, F., Kats, M. A., Genevet, P., and Capasso, F. 2015. Multiwavelength achromatic metasurfaces by dispersive phase compensation. Science 347, 6228, 1342–1345.Google Scholar
    3. Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3, 1, 1–122. Google ScholarDigital Library
    4. Chakrabarti, A., and Zickler, T. 2011. Statistics of real-world hyperspectral images. In Proc. CVPR, IEEE, 193–200. Google ScholarDigital Library
    5. Chan, S. H., Khoshabeh, R., Gibson, K. B., Gill, P. E., and Nguyen, T. Q. 2011. An augmented lagrangian method for total variation video restoration. IEEE Trans. Image Process. 20, 11, 3097–3111. Google ScholarDigital Library
    6. Eberhart, R. C., and Shi, Y. 2001. Particle swarm optimization: developments, applications and resources. In Proc. of 2001 Congress on Evolutionary Computation, IEEE, 81–86.Google Scholar
    7. Fischer, R. E., Tadic-Galeb, B., Yoder, P. R., and Galeb, R. 2008. Optical system design. McGraw Hill. Google ScholarDigital Library
    8. Gill, P. R., and Stork, D. G. 2013. Lensless ultra-miniature imagers using odd-symmetry spiral phase gratings. In Computational Optical Sensing and Imaging, OSA, CW4C-3.Google Scholar
    9. Goodman, J. 2008. Introduction to Fourier optics. McGraw-hill.Google Scholar
    10. Heide, F., Rouf, M., Hullin, M. B., Labitzke, B., Heidrich, W., and Kolb, A. 2013. High-quality computational imaging through simple lenses. ACM Trans. Graph. 32, 5, 149. Google ScholarDigital Library
    11. Jiang, W., Wang, J., and Dong, X. 2013. A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping. Opt. Laser Tech. 45, 37–41.Google ScholarCross Ref
    12. Joshi, N., Szeliski, R., and Kriegman, D. 2008. Psf estimation using sharp edge prediction. In Proc. CVPR, IEEE, 1–8.Google Scholar
    13. Kang, G., Tan, Q., Wang, X., and Jin, G. 2010. Achromatic phase retarder applied to mwir & lwir dual-band. Opt. Express 18, 2, 1695–1703.Google ScholarCross Ref
    14. Kim, G., Domínguez-Caballero, J. A., and Menon, R. 2012. Design and analysis of multi-wavelength diffractive optics. Opt. Express 20, 3, 2814–2823.Google ScholarCross Ref
    15. Krishnan, D., and Fergus, R. 2009. Fast image deconvolution using hyper-laplacian priors. In Advances in Neural Information Processing Systems, NIPS, 1033–1041.Google Scholar
    16. Krishnan, D., Tay, T., and Fergus, R. 2011. Blind deconvolution using a normalized sparsity measure. In Proc. CVPR, IEEE, 233–240. Google ScholarDigital Library
    17. Meyers, M. M., 1998. Hybrid refractive/diffractive achromatic camera lens, Feb. 3. US Patent 5,715,091.Google Scholar
    18. Monjur, M., Spinoulas, L., Gill, P. R., and Stork, D. G. 2015. Ultra-miniature, computationally efficient diffractive visual-bar-position sensor. In Proc. SensorComm, IEIFSA.Google Scholar
    19. Muzychenko, Y. B., Zinchik, A., Stafeev, S., and Tomilin, M. 2011. Fractal diffraction elements with variable transmittance and phase shift. In International Commission for Optics (ICO 22), International Society for Optics and Photonics, 80112F–80112F.Google Scholar
    20. Nakai, T., and Ogawa, H. 2002. Research on multi-layer diffractive optical elements and their application to camera lenses. In Diffractive Optics and Micro-Optics, OSA, DMA2.Google Scholar
    21. Nikonorov, A., Skidanov, R., Fursov, V., Petrov, M., Bibikov, S., and Yuzifovich, Y. 2015. Fresnel lens imaging with post-capture image processing. In Proc. CVPR Workshops, IEEE, 33–41.Google Scholar
    22. Peng, Y., Fu, Q., Amata, H., Su, S., Heide, F., and Heidrich, W. 2015. Computational imaging using lightweight diffractive-refractive optics. Opt. Express 23, 24, 31393–31407.Google ScholarCross Ref
    23. Quirin, S., and Piestun, R. 2013. Depth estimation and image recovery using broadband, incoherent illumination with engineered point spread functions {invited}. Applied optics 52, 1, A367–A376.Google Scholar
    24. Samei, E., Flynn, M. J., and Reimann, D. A. 1998. A method for measuring the presampled mtf of digital radiographic systems using an edge test device. Medical physics 25, 1, 102–113.Google Scholar
    25. Schuler, C. J., Hirsch, M., Harmeling, S., and Scholkopf, B. 2011. Non-stationary correction of optical aberrations. In Proc. ICCV, IEEE, 659–666. Google ScholarDigital Library
    26. Schuler, C. J., Burger, H. C., Harmeling, S., and Scholkopf, B. 2013. A machine learning approach for non-blind image deconvolution. In Proc. CVPR, IEEE, 1067–1074. Google ScholarDigital Library
    27. Schwartzburg, Y., Testuz, R., Tagliasacchi, A., and Pauly, M. 2014. High-contrast computational caustic design. ACM Trans. Graph. 33, 4, 74. Google ScholarDigital Library
    28. Shan, Q., Jia, J., and Agarwala, A. 2008. High-quality motion deblurring from a single image. ACM Trans. Graph. 27, 3, 73. Google ScholarDigital Library
    29. Singh, M., Tervo, J., and Turunen, J. 2014. Broadband beam shaping with harmonic diffractive optics. Opt. Express 22, 19, 22680–22688.Google ScholarCross Ref
    30. Skauli, T., and Farrell, J. 2013. A collection of hyperspectral images for imaging systems research. In Proc. IS&T/SPIE Electronic Imaging, SPIE, 86600C–86600C.Google Scholar
    31. Smith, W. J. 2005. Modern lens design. McGraw-Hill.Google Scholar
    32. Stork, D. G., and Gill, P. R. 2014. Optical, mathematical, and computational foundations of lensless ultra-miniature diffractive imagers and sensors. International Journal on Advances in Systems and Measurements 7, 3, 4.Google Scholar
    33. Su, Z., Zeng, K., Liu, L., Li, B., and Luo, X. 2014. Corruptive artifacts suppression for example-based color transfer. IEEE Trans. Multimedia 16, 4, 988–999. Google ScholarDigital Library
    34. Wang, Y., Yun, W., and Jacobsen, C. 2003. Achromatic fresnel optics for wideband extreme-ultraviolet and x-ray imaging. Nature 424, 6944, 50–53.Google Scholar
    35. Wang, P., Mohammad, N., and Menon, R. 2016. Chromaticaberration-corrected diffractive lenses for ultra-broadband focusing. Scientific reports 6.Google Scholar
    36. Yamaguchi, M., Haneishi, H., and Ohyama, N. 2008. Beyond red–green–blue (rgb): Spectrum-based color imaging technology. JIST 52, 1, 10201–1.Google ScholarCross Ref
    37. Ye, G., Jolly, S., Bove Jr, V. M., Dai, Q., Raskar, R., and Wetzstein, G. 2014. Toward bxdf display using multilayer diffraction. ACM Trans. Graph. 33, 6, 191. Google ScholarDigital Library
    38. Yuan, L., Sun, J., Quan, L., and Shum, H.-Y. 2008. Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph. 27, 3, 74. Google ScholarDigital Library
    39. Yue, T., Suo, J., Wang, J., Cao, X., and Dai, Q. 2015. Blind optical aberration correction by exploring geometric and visual priors. In Proc. CVPR, IEEE, 1684–1692.Google Scholar
    40. Zhou, G., Chen, Y., Wang, Z., and Song, H. 1999. Genetic local search algorithm for optimization design of diffractive optical elements. Appl. Opt. 38, 20, 4281–4290.Google ScholarCross Ref


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