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Computational Imaging for Phase Retrieval and Biomedical Applications


Shen, Cheng (2023) Computational Imaging for Phase Retrieval and Biomedical Applications. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/pahb-cx81.


In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges.

The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging.

The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Computational imaging; Phase retrieval; Deep learning
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Yang, Changhuei
Thesis Committee:
  • Wang, Lihong (chair)
  • Yang, Changhuei
  • Bouman, Katherine L.
  • Zernicka-Goetz, Magdalena
Defense Date:22 May 2023
Funding AgencyGrant Number
Caltech Innovation Initiative (CII)25570015
Donna and Benjamin M. Rosen Bioengineering Center9900050
Caltech Center for Sensing to Intelligence (S2I) Funding13520296
Heritage Research Institute for the Advancement of Medicine and Science at Caltech (HMRI) FundingHMRI-15-09-01
Merkin Translational Research Grant2021
Record Number:CaltechTHESIS:05272023-062623589
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Ch. 2 adapted for Ch. 3 adapted for Ch. 5 adapted for Ch. 6
Shen, Cheng0000-0001-7136-4715
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15214
Deposited By: Cheng Shen
Deposited On:01 Jun 2023 15:54
Last Modified:01 Dec 2023 18:13

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