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Image Deblurring for Material Science Applications in Optical Microscopy

Ambrozic, Courtney Lynn
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1532625732841875

Abstract Details

2018, Master of Science (M.S.), University of Dayton, Electrical Engineering.
The objective of this research is to develop an application-specific image deblurring algorithm for microscopic, material images. In microscopy, there are two types of image blur---one due to the limitation of the microscope, and another due to defocus. Defocus blur is particularly problematic in the case of spatially-varying materials, where the texture of the material surface is not flat. Through various deconvolution techniques, the image can be deblurred and high frequency components can be restored. Through our partnership with the Materials and Manufacturing Directorate at Air Force Research Lab (AFRL), we have developed an optimal deblurring method specifically for material images. We tailor our deblurring method for material images based on a priori knowledge about the characteristics of the material. The specificity of the material features allows us to impose stronger constraints on the defocus blur, which we leverage to handle spatially varying material surfaces, whose defocus blur is non-uniform across the image. The significance of this research is the development of a deblurring algorithm capable of handling a larger amount of blur and noise than the state-of-the-art methods. Currently, existing image deblurring algorithms are designed to handle diverse scene contents and blur kernels with large degrees of freedom. As a result, the existing methods can handle only small amounts of blur and noise. With the goal to handle types of images acquired by a specific microscope modality, we are able to recover finer details within the image while handling a larger degree of blur because the solution space is significantly constrained.
Keigo Hirakawa (Advisor)
Craig Przybyla (Committee Member)
Jeff Simmons (Committee Member)
53 p.
Computer Engineering; Computer Science; Electrical Engineering; Engineering
Image Deblurring, Deconvolution, Optical Microscopy, Image Processing, Regularization

Recommended Citations

Citations

  • Ambrozic, C. L. (2018). Image Deblurring for Material Science Applications in Optical Microscopy [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1532625732841875

    APA Style (7th edition)

  • Ambrozic, Courtney. Image Deblurring for Material Science Applications in Optical Microscopy. 2018. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1532625732841875.

    MLA Style (8th edition)

  • Ambrozic, Courtney. "Image Deblurring for Material Science Applications in Optical Microscopy." Master's thesis, University of Dayton, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1532625732841875

    Chicago Manual of Style (17th edition)

dayton1532625732841875
663
© 2018, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.