Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year

2019

Author dc:creator

• Martin-King, Chloe

Contributors dc:contributor

• Mohamed Allali
• Erik Linstead
• Hesham El-Askary
• Mohammad Kamal

• image restoration
• region hiding
• biomedical image segmentation
• convolutional neural network
• object removal
• inpainting
• Artificial Intelligence and Robotics
• Numerical Analysis and Scientific Computing
• Other Analytical, Diagnostic and Therapeutic Techniques and Equipment
• Other Applied Mathematics
• Partial Differential Equations
• Pathology
• Signal Processing

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/3

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1003