Abstract

Lensless imaging provides opportunities to design imaging systems free from the constraints imposed by traditional camera architectures. Due to advances in imaging hardware, fabrication techniques, and new algorithms, researchers have recently developed lensless imaging systems that are extremely compact and lightweight or able to image higher-dimensional quantities. Here we review these recent advances and describe the design principles and their effects that one should consider when developing and using lensless imaging systems.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2021 (3)

2020 (10)

N. Shekel and O. Katz, “Using fiber-bending-generated speckles for improved working distance and background rejection in lensless micro-endoscopy,” Opt. Lett. 45, 4288–4291 (2020).
[Crossref]

S. Jiang, J. Zhu, P. Song, C. Guo, Z. Bian, R. Wang, Y. Huang, S. Wang, H. Zhang, and G. Zheng, “Wide-field, high-resolution lensless on-chip microscopy: via near-field blind ptychographic modulation,” Lab Chip 20, 1058–1065 (2020).
[Crossref]

J. R. Miller, C. Y. Wang, C. D. Keating, and Z. Liu, “Particle-based reconfigurable scattering masks for lensless imaging,” ACS Nano 14, 13038–13046 (2020).
[Crossref]

V. Boominathan, J. K. Adams, J. T. Robinson, and A. Veeraraghavan, “PhlatCam: designed phase-mask based thin lensless camera,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 1618–1629 (2020).
[Crossref]

G. Kuo, F. L. Liu, I. Grossrubatscher, R. Ng, and L. Waller, “On-chip fluorescence microscopy with a random microlens diffuser,” Opt. Express 28, 8384–8399 (2020).
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F. Linda Liu, G. Kuo, N. Antipa, K. Yanny, and L. Waller, “Fourier DiffuserScope: single-shot 3D Fourier light field microscopy with a diffuser,” Opt. Express 28, 28969–28986 (2020).
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Y. Hua, S. Nakamura, M. S. Asif, and A. C. Sankaranarayanan, “SweepCam—depth-aware lensless imaging using programmable masks,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 1606–1617 (2020).
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2019 (5)

Y. Wu, M. K. Sharma, and A. Veeraraghavan, “WISH: wavefront imaging sensor with high resolution,” Light Sci. Appl. 8, 2047–7538 (2019).
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O. Liba, K. Murthy, Y.-T. Tsai, T. Brooks, T. Xue, N. Karnad, Q. He, J. T. Barron, D. Sharlet, R. Geiss, S. W. Hasinoff, Y. Pritch, and M. Levoy, “Handheld mobile photography in very low light,” ACM Trans. Graph. 38, 1–16 (2019).
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K. Monakhova, J. Yurtsever, G. Kuo, N. Antipa, K. Yanny, and L. Waller, “Learned reconstructions for practical mask-based lensless imaging,” Opt. Express 27, 28075–28090 (2019).
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2018 (7)

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J. Tan, L. Niu, J. K. Adams, V. Boominathan, J. T. Robinson, R. G. Baraniuk, and A. Veeraraghavan, “Face detection and verification using lensless cameras,” IEEE Trans. Comput. Imaging 5, 180–194 (2018).
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N. Wadhwa, R. Garg, D. E. Jacobs, B. E. Feldman, N. Kanazawa, R. Carroll, Y. Movshovitz-Attias, J. T. Barron, Y. Pritch, and M. Levoy, “Synthetic depth-of-field with a single-camera mobile phone,” ACM Trans. Graph. 37, 1–13 (2018).
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2017 (6)

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2016 (5)

A. Porat, E. R. Andresen, H. Rigneault, D. Oron, S. Gigan, and O. Katz, “Widefield lensless imaging through a fiber bundle via speckle correlations,” Opt. Express 24, 16835–16855 (2016).
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2015 (3)

M. J. DeWeert and B. P. Farm, “Lensless coded-aperture imaging with separable Doubly-Toeplitz masks,” Opt. Eng. 54, 023102 (2015).
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2014 (3)

A. Greenbaum, Y. Zhang, A. Feizi, P.-L. Chung, W. Luo, S. R. Kandukuri, and A. Ozcan, “Wide-field computational imaging of pathology slides using lens-free on-chip microscopy,” Sci. Transl. Med. 6, 267ra175 (2014).
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D. Wu, G. Wetzstein, C. Barsi, T. Willwacher, Q. Dai, and R. Raskar, “Ultra-fast lensless computational imaging through 5D frequency analysis of time-resolved light transport,” Int. J. Comput. Vis. 110, 128–140 (2014).
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2013 (2)

Z. Gorocs and A. Ozcan, “On-chip biomedical imaging,” IEEE Rev. Biomed. Eng. 6, 29–46 (2013).
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2012 (4)

A. Kirmani, H. Jeelani, V. Montazerhodjat, and V. K. Goyal, “Diffuse imaging: creating optical images with unfocused time-resolved illumination and sensing,” IEEE Signal Process. Lett. 19, 31–34 (2012).
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A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12, 1242 (2012).
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A. Greenbaum and A. Ozcan, “Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy,” Opt. Express 20, 3129–3143 (2012).
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A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging,” Nat. Commun. 3, 745 (2012).
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2011 (3)

W. Chi and N. George, “Optical imaging with phase-coded aperture,” Opt. Express 19, 4294–4300 (2011).
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W. Bishara, U. Sikora, O. Mudanyali, T.-W. Su, O. Yaglidere, S. Luckhart, and A. Ozcan, “Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array,” Lab Chip 11, 1276–1279 (2011).
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2010 (4)

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10, 1417–1428 (2010).
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W. Bishara, T.-W. Su, A. F. Coskun, and A. Ozcan, “Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution,” Opt. Express 18, 11181–11191 (2010).
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2009 (4)

S. Seo, T. W. Su, D. K. Tseng, A. Erlinger, and A. Ozcan, “Lensfree holographic imaging for on-chip cytometry and diagnostics,” Lab Chip 9, 777–787 (2009).
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2008 (4)

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2006 (5)

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2005 (1)

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V. Boominathan, J. K. Adams, J. T. Robinson, and A. Veeraraghavan, “PhlatCam: designed phase-mask based thin lensless camera,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 1618–1629 (2020).
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Y. Hua, S. Nakamura, M. S. Asif, and A. C. Sankaranarayanan, “SweepCam—depth-aware lensless imaging using programmable masks,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 1606–1617 (2020).
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J. Tan, L. Niu, J. K. Adams, V. Boominathan, J. T. Robinson, R. G. Baraniuk, and A. Veeraraghavan, “Face detection and verification using lensless cameras,” IEEE Trans. Comput. Imaging 5, 180–194 (2018).
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V. Boominathan, J. K. Adams, J. T. Robinson, and A. Veeraraghavan, “PhlatCam: designed phase-mask based thin lensless camera,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 1618–1629 (2020).
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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (9)

Fig. 1.
Fig. 1. Lensless cameras can be classified into several types: illumination-modulated refers to controlled illumination, mask-modulated refers to using a fixed amplitude/phase plate (modulator) in front of the sensor, and programmable-modulator systems have a programmable dynamic spatial light modulator (SLM) in front of the sensor. All types use a computational algorithm to reconstruct the scene from the captured 2D image.
Fig. 2.
Fig. 2. Illumination-modulated lensless microscopy systems. (A) In this case, the sample is placed close to the sensor and trans-illuminated by a light source placed much farther from the sample (modified from [20]). (B) Shadow imaging super-resolution by combining multiple low-resolution frames with sub-pixel shifts (image modified from [58]). (C) Wide field of view holographic imaging [67]. (D) Color holographic imaging by combining reconstruction from three wavelengths of illumination (image modified from [68]). (C) and (D) show the captured hologram, reconstructed lensless image, and comparison with the image taken with a high-NA microscope.
Fig. 3.
Fig. 3. Types of masks used in mask-modulated lensless imaging systems. Binary amplitude masks either block or allow light to pass through, giving amplitude modulation of either zero or one. Binary phase gratings have two heights of transparent material, giving phase modulation of either zero or $\pi$. Diffusers have continuous but random surface heights for continuous phase modulation. Multi-level phase masks have “${n}$” discrete material heights, giving discrete phase modulations.
Fig. 4.
Fig. 4. (Top) Images of various lensless camera prototypes. (A) FlatScope (image modified from [27]. (B) FlatCam [22] (image modified from [11]). (C) Fresnel zone aperture (FZA)-based lensless imager (image modified from [77]). (D) Random microlens diffuser microscope (image modified from [25]). (Bottom) Images of various masks used. (E) Separable amplitude mask (image modified from [27]). (F) Amplitude mask with multiple FZAs of different fringe phases (image modified from [77]). (G) Spiral phase gratings (image modified from [79]). (H) Phase mask that generates contour PSF (image modified from [23]).
Fig. 5.
Fig. 5. Example mask-modulated lensless imaging results. Microscopy: (A) modified from [25], (B) modified from [23]. Photography: (C) modified from [103], (D) modified from [23]. (E) Light field and re-focusing, modified from [46]. (F) Thermal imaging, modified from [28]. Compressive imaging: (G) 3D image, modified from [53], (H) video from single capture, modified from [34], and (I) spectral imaging from single capture, modified from [33].
Fig. 6.
Fig. 6. Experimental point-spread functions (PSFs) of mask-modulated lensless systems. (A) Separable PSF, generated by the amplitude mask in [21]. (B) Spiral PSF of binary phase gratings, used in [79]. (C) Caustic PSF of diffuser used in DiffuserCam [53]. (D) Contour PSF of phase mask used in PhlatCam [23]. (E) Sparse URA PSF, by amplitude mask, used in [84]. All images were modified from respective references.
Fig. 7.
Fig. 7. Reconfigurable nanoparticles to achieve dynamic scattering masks [98]. The particles are silica-coated gold nanowires suspended in water to form a scattering mask. The nanowires are highly polarizable, exhibit a strong response to the applied electric field, and can be oriented in different ways depending on the direction of the applied field. Image modified from [98].
Fig. 8.
Fig. 8. Data-driven approaches to reconstruct lensless images: (A) modified from [103], (B) modified from [104], (C) modified from [106].
Fig. 9.
Fig. 9. Reconstruction results using data-driven techniques. ADMM and TV-ADMM use iterative convex optimization techniques. Le-ADMM-U [104] and FlatNet [103], outlined in green box, use feed-forward neural network, trained with data, to reconstruct. The data-driven methods drastically outperform, in quality, the optimization techniques. (A) Modified from [104] and (B) modified from [103].

Equations (7)

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M = d z ,
θ F o V = min ( 2 θ C R A , 2 tan 1 ( s / ( 2 d ) ) ) ,
y = Hx ,
Y = X h ,
Y = Φ L X Φ R T ,
y = z H z x z , Y = z X z h z , Y = z Φ L z X z Φ R z T ,
x ^ = arg min x | | y f H ( x ) | | 2 + λ R ( x ) ,