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Efficient Perceptual Super-Resolution

Abstract Super-Resolution (SR) techniques are widely developed to increase image resolution by fusing several Low-Resolution (LR) images of the same scene to overcome sensor hardware limitations and reduce media impairments in a cost-effective manner. When choosing a solution for the SR problem, there is always a trade-off between computational efficiency and High-Resolution (HR) image quality. Existing SR approaches suffer from extremely high computational requirements due to the high number of unknowns to be estimated in the solution of the SR inverse problem. This thesis proposes efficient iterative SR techniques based on Visual Attention (VA) and perceptual modeling of the human visual system. In the first part of this thesis, an efficient ATten... (more)
Created Date 2011
Contributor Sadaka, Nabil Gergi (Author) / Karam, Lina J (Advisor) / Spanias, Andreas S (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Abousleman, Glen P (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / luminance masking / MAP estimator / perceptual image processing / Super resolution / visual attention
Type Doctoral Dissertation
Extent 184 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis