title: Blind image blur assessment using singular value similarity and blur comparisons.
abstract: The increasing number of demanding consumer image applications has led to increased interest in no-reference objective image quality assessment (IQA) algorithms. In this paper, we propose a new blind blur index for still images based on singular value similarity. The algorithm consists of three steps. First, a re-blurred image is produced by applying a Gaussian blur to the test image. Second, a singular value decomposition is performed on the test image and re-blurred image. Finally, an image blur index is constructed based on singular value similarity. The experimental results obtained on four simulated databases to demonstrate that the proposed algorithm has high correlation with human judgment when assessing blur or noise distortion of images.
authors.affiliation: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China.
authors.forename: Qing-Bing
authors.initials: QB
authors.lastname: Sang
authors.affiliation: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China.
authors.forename: Xiao-Jun
authors.initials: XJ
authors.lastname: Wu
authors.affiliation: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu, China.
authors.forename: Chao-Feng
authors.initials: CF
authors.lastname: Li
authors.affiliation: Computer Science Department, Texas Tech University, Lubbock, Texas, United States of America.
authors.forename: Yin
authors.initials: Y
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted