Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Teng-Sheng Moh

Second Advisor

Katerina Potika

Third Advisor

Kevin Smith

Keywords

Generative Adversarial Network, Convolution Neural Network, Image Completion, Image In-painting

Abstract

This paper presents a method for image completion, an active research area in the field of computer vision. The method described in the paper aims at achieving comparable results to other state of the art methods with approximately four and a half times reduction in training time. It is a two step procedure which involves image completion and enhancing the resolution of the completed image. We use the SSIM metric to evaluate the quality of the completed image and to also time our model against other image completion models.

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