Image Restoration Using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Te...

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Image Restoration Using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique "
Martin-King, Chloe Furness Allali, Mohamed

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1052946
Doc. No	:	TL52063
Main Entry	:	Martin-King, Chloe Furness
Title & Author	:	Image Restoration Using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique\ Martin-King, Chloe FurnessAllali, Mohamed
College	:	Chapman University
Date	:	2019
Degree	:	Ph.D.
student score	:	2019
Note	:	142 p.
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.
Descriptor	:	Artificial intelligence
	:	Histology
	:	Mathematics
Added Entry	:	Allali, Mohamed
Added Entry	:	Chapman University