Date of Award
Winter 12-4-2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computational and Data Sciences
First Advisor
Mohamed Allali
Second Advisor
Erik Linstead
Third Advisor
Hesham El-Askary
Fourth Advisor
Mohammad Kamal
Abstract
In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also effective in object removal tasks. Lastly, we present a segmentation system for distinguishing glands, stroma, and cells in slide images, in addition to current results, as one component of an ongoing project to aid in colon cancer prognostication.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
C. Martin-King, "Image restoration using automatic damaged regions detection and machine learning-based inpainting technique," Ph.D. dissertation, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000114
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