Thesis Details
Image Restoration Based on Convolutional Neural Networks
A merit of this thesis is to introduce a unified image restoration approach based on a convolutional neural network which is to some degree degradation type independent. Convolutional neural network models were trained for two different tasks, a motion deblurring of license plate images and a removal of artifacts related to lossy image compression. The capabilities of such models are studied from two main perspectives. Firstly, how well the model can restore an image compared to the state-of-the-art methods. Secondly, what is the model's ability to handle several ranges of the same degradation type.
An idea of the unified end-to-end approach is based on a recent development of neural networks and related deep learning in a field of computer vision. The existing hand-engineered methods of image restoration are often highly specialized for a given degradation type and in fact, define state of the art in several image restoration tasks. The end-to-end approach allows to directly train the required model on specifically corrupted images, and, further, to restore various levels of corruption with a single model.
For motion deblurring, the end-to-end mapping model derived from models used in computer vision is deployed. Compression artifacts are restored with similar end-to-end based model further enhanced using specialized objective functions together with a network skip architecture.
A direct comparison of the convolutional network based models and engineered methods shows that the data-driven approach provides beyond state-of-the-art results with a high ability to generalize over different levels of degradations. Based on the achieved results, this work presents the convolutional neural network based methods suggesting a possibility having the unified approach used for wide range of image restoration tasks.
convolutional neural networks, deep learning, image restoration, motion deblurring, JPEG artifacts
@phdthesis{FITPT304,
author = "Pavel Svoboda",
type = "Ph.D. thesis",
title = "Image Restoration Based on Convolutional Neural Networks",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2017,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/phd-thesis/304/"
}