公开(公告)号：US20240310782A1

公开(公告)日：2024-09-19

申请号：US18546095

申请日：2022-02-09

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Luzhe Huang ,  Tairan Liu

IPC: G03H1/08 ,  G03H1/00 ,  G03H1/04 ,  G06T5/50 ,  G06T5/60

CPC classification number: G03H1/0866 ,  G03H1/0005 ,  G03H1/0443 ,  G06T5/50 ,  G06T5/60 ,  G03H2001/005 ,  G03H2001/0458 ,  G03H2001/0883 ,  G03H2210/55 ,  G06T2207/10056 ,  G06T2207/20084 ,  G06T2207/30024

Abstract: Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important step in holographic image reconstruction. A convolutional recurrent neural network (RNN)-based phase recovery approach is employed that uses multiple holograms, captured at different sample-to-sensor distances to rapidly reconstruct the phase and amplitude information of a sample, while also performing autofocusing through the same trained neural network. The success of this deep learning-enabled holography method is demonstrated by imaging microscopic features of human tissue samples and Papanicolaou (Pap) smears. These results constitute the first demonstration of the use of recurrent neural networks for holographic imaging and phase recovery, and compared with existing methods, the presented approach improves the reconstructed image quality, while also increasing the depth-of-field and inference speed.

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.