公开(公告)号：US11514325B2

公开(公告)日：2022-11-29

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: G06N3/08 ,  G06N3/02 ,  G03H1/22 ,  G03H1/04 ,  G06N3/067 ,  G03H1/00

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：US11222415B2

公开(公告)日：2022-01-11

申请号：US16395674

申请日：2019-04-26

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

IPC: G06T5/50 ,  G06N3/08 ,  G06T3/40 ,  G06T5/00

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

公开(公告)号：US12190478B2

公开(公告)日：2025-01-07

申请号：US17530471

申请日：2021-11-19

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

IPC: G06T5/50 ,  G06N3/08 ,  G06T3/4046 ,  G06T3/4053 ,  G06T3/4076 ,  G06T5/70 ,  G06T5/73 ,  G06T5/92 ,  G06T7/00

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

公开(公告)号：US20190333199A1

公开(公告)日：2019-10-31

申请号：US16395674

申请日：2019-04-26

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

IPC: G06T5/50 ,  G06N3/08 ,  G06T3/40 ,  G06T5/00

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

公开(公告)号：US20220114711A1

公开(公告)日：2022-04-14

申请号：US17530471

申请日：2021-11-19

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Harun Gunaydin ,  Kevin de Haan

IPC: G06T5/50 ,  G06T7/00 ,  G06T3/40

Abstract: A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and/or image contrast.

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.