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Title: | Learning to super-resolve images using self-similarities |
Author(s): | Singh, Abhishek |
Director of Research: | Ahuja, Narendra |
Doctoral Committee Chair(s): | Ahuja, Narendra |
Doctoral Committee Member(s): | Forsyth, David A.; Do, Minh N.; Hasegawa-Johnson, Mark A. |
Department / Program: | Electrical & Computer Eng |
Discipline: | Electrical & Computer Engr |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | Ph.D. |
Genre: | Dissertation |
Subject(s): | Self-Similarity
Image Enhancement Image Super-Resolution |
Abstract: | The single image super-resolution problem entails estimating a high-resolution version of a low-resolution image. Recent studies have shown that high resolution versions of the patches of a given low-resolution image are likely to be found within the given image itself. This recurrence of patches across scales in an image forms the basis of self-similarity driven algorithms for image super-resolution. Self-similarity driven approaches have the appeal that they do not require any external training set; the mapping from low-resolution to high-resolution is obtained using the cross scale patch recurrence. In this dissertation, we address three important problems in super-resolution, and present novel self-similarity based solutions to them: First, we push the state-of-the-art in terms of super-resolution of fine textural details in the scene. We propose two algorithms that use self-similarity in conjunction with the fact that textures are better characterized by their responses to a set of spatially localized bandpass filters, as compared to intensity values directly. Our proposed algorithms seek self-similarities in the sub-bands of the image, for better synthesizing fine textural details. Second, we address the problem of super-resolving an image in the presence of noise. To this end, we propose the first super-resolution algorithm based on self-similarity that effectively exploits the high-frequency content present in noise (which is ordinarily discarded by denoising algorithms) for synthesizing useful textures in high-resolution. Third, we present an algorithm that is able to better super-resolve images containing geometric regularities such as in urban scenes, cityscapes etc. We do so by extracting planar surfaces and their parameters (mid-level cues) from the scene and exploiting the detected scene geometry for better guiding the self-similarity search process. Apart from the above self-similarity algorithms, this dissertation also presents a novel edge-based super-resolution algorithm that super-resolves an image by learning from training data how edge profiles transform across resolutions. We obtain edge profiles via a detailed and explicit examination of local image structure, which we show to be more robust and accurate as compared to conventional gradient profiles. |
Issue Date: | 2015-03-06 |
Type: | Text |
URI: | http://hdl.handle.net/2142/78325 |
Rights Information: | Copyright 2015 Abhishek Singh |
Date Available in IDEALS: | 2015-07-22 |
Date Deposited: | May 2015 |
This item appears in the following Collection(s)
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Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering -
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois