公开(公告)号：US20220206434A1

公开(公告)日：2022-06-30

申请号：US17604416

申请日：2020-04-21

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Tairan Liu ,  Yibo Zhang ,  Zhensong Wei

IPC: G03H1/08 ,  G03H1/04 ,  G03H1/26 ,  G06N3/08 ,  G06N3/04

Abstract: A method for performing color image reconstruction of a single super-resolved holographic sample image includes obtaining a plurality of sub-pixel shifted lower resolution hologram images of the sample using an image sensor by simultaneous illumination at multiple color channels. Super-resolved hologram intensity images for each color channel are digitally generated based on the lower resolution hologram images. The super-resolved hologram intensity images for each color channel are back propagated to an object plane with image processing software to generate a real and imaginary input images of the sample for each color channel. A trained deep neural network is provided and is executed by image processing software using one or more processors of a computing device and configured to receive the real input image and the imaginary input image of the sample for each color channel and generate a color output image of the sample.

公开(公告)号：US20190294108A1

公开(公告)日：2019-09-26

申请号：US16359609

申请日：2019-03-20

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Yichen Wu ,  Yibo Zhang ,  Harun Gunaydin

IPC: G03H1/22 ,  G03H1/04 ,  G06N3/08

Abstract: A method of performing phase retrieval and holographic image reconstruction of an imaged sample includes obtaining a single hologram intensity image of the sample using an imaging device. The single hologram intensity image is back-propagated to generate a real input image and an imaginary input image of the sample with image processing software, wherein the real input image and the imaginary input image contain twin-image and/or interference-related artifacts. A trained deep neural network is provided that is executed by the image processing software using one or more processors and configured to receive the real input image and the imaginary input image of the sample and generate an output real image and an output imaginary image in which the twin-image and/or interference-related artifacts are substantially suppressed or eliminated. In some embodiments, the trained deep neural network simultaneously achieves phase-recovery and auto-focusing significantly extending the DOF of holographic image reconstruction.

公开(公告)号：US20190137932A1

公开(公告)日：2019-05-09

申请号：US16302087

申请日：2017-05-24

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yibo Zhang ,  Seung Yoon Lee ,  John D. Fitzgerald

IPC: G03H1/04 ,  G03H1/00 ,  G03H1/08 ,  H04N5/232 ,  G06T5/50

Abstract: A method of imaging a sample having birefringent crystals (or other materials) using a lens-free polarized microscopy device includes illuminating the sample contained on a sample holder with circularly polarized partially coherent or coherent light and capturing lower resolution holographic images of the birefringent crystals with an image sensor. A polarization analyzer unit made from a λ/4 retarder and a linear polarizer is positioned between the sample holder and the image sensor. The lower resolution holographic images are obtained with the polarization analyzer unit in two different orientations (e.g. ˜90° orientations). Phase-retrieved, higher resolution images of the birefringent crystals at the different orientations are obtained using the lower resolution holographic images. A differential image is generated from the respective phase-retrieved, higher resolution images. An object support mask is applied to identify the birefringent crystals which can then be pseudo-colored.

公开(公告)号：US20170220000A1

公开(公告)日：2017-08-03

申请号：US15500880

申请日：2015-07-31

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Alon Greenbaum ,  Yibo Zhang ,  Alborz Feizi ,  Wei Luo

IPC: G03H1/00 ,  G03H1/08 ,  H04N5/232 ,  G03H1/04 ,  G01N15/14 ,  H04N5/225

CPC classification number: G03H1/0005 ,  G01N15/1429 ,  G01N15/1434 ,  G01N15/1475 ,  G01N2015/1006 ,  G01N2015/144 ,  G01N2015/1445 ,  G01N2015/1454 ,  G03H1/0443 ,  G03H1/0866 ,  G03H1/0891 ,  G03H2001/005 ,  G03H2001/0447 ,  G03H2001/0454 ,  G03H2001/046 ,  G03H2001/0816 ,  G03H2001/0883 ,  G03H2001/2655 ,  G03H2210/42 ,  G03H2226/02 ,  G03H2227/03 ,  H04N5/2256 ,  H04N5/23238

Abstract: A method for lens-free imaging of a sample or objects within the sample uses multi-height iterative phase retrieval and rotational field transformations to perform wide FOV imaging of pathology samples with clinically comparable image quality to a benchtop lens-based microscope. The solution of the transport-of-intensity (TIE) equation is used as an initial guess in the phase recovery process to speed the image recovery process. The holographically reconstructed image can be digitally focused at any depth within the object FOV (after image capture) without the need for any focus adjustment, and is also digitally corrected for artifacts arising from uncontrolled tilting and height variations between the sample and sensor planes. In an alternative embodiment, a synthetic aperture approach is used with multi-angle iterative phase retrieval to perform wide FOV imaging of pathology samples and increase the effective numerical aperture of the image.