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Perceptual-Based Locally Adaptive Noise and Blur Detection

Abstract The quality of real-world visual content is typically impaired by many factors including image noise and blur. Detecting and analyzing these impairments are important steps for multiple computer vision tasks. This work focuses on perceptual-based locally adaptive noise and blur detection and their application to image restoration.

In the context of noise detection, this work proposes perceptual-based full-reference and no-reference objective image quality metrics by integrating perceptually weighted local noise into a probability summation model. Results are reported on both the LIVE and TID2008 databases. The proposed metrics achieve consistently a good performance across noise types and across databases as compared to many of the best ve... (more)
Created Date 2016
Contributor Zhu, Tong (Author) / Karam, Lina (Advisor) / Li, Baoxin (Committee member) / Bliss, Daniel (Committee member) / Myint, Soe (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / blur detection / deblur / noise detection / quality assessment / quality metric
Type Doctoral Dissertation
Extent 115 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis