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Image blur classification and blur usefulness assessment
2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2017Blur classification is an important and widely-studied problem in computer vision. State-of-the-art blur classification methods are designed and verified using man-made blur images of known blur types and blur kernels. In reality, natural blur occurs under wild-conditions, and thus cannot be simply simulated by several hand-crafted blur kernels. Hence,
Mingyuan Fan 0001 +3 more
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MC-Blur: A Comprehensive Benchmark for Image Deblurring
IEEE Transactions on Circuits and Systems for Video TechnologyBlur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion, and defocus.
Wenqi Ren, Tao Wang, Wei Liu
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Curator: The Museum Journal, 1995
ABSTRACTOur certainty about the definition of museums is disappearing and with it goes our assurance about where we are and what we are becoming. Observing visitors' use of the United States Holocaust Memorial Museum could cause us to change our understanding about how people use and act in museums.
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ABSTRACTOur certainty about the definition of museums is disappearing and with it goes our assurance about where we are and what we are becoming. Observing visitors' use of the United States Holocaust Memorial Museum could cause us to change our understanding about how people use and act in museums.
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Image Transformations and Blurring
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities.
Justin Domke, Yiannis Aloimonos
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