Paper Info
Title
On benchmarking non-blind deconvolution algorithms: A sample driven comparison of image de-blurring methods for automated visual inspection systems
Abstract
This paper discusses motion blur reduction in digital images as a pre-processing step for automated visual inspection (AVI) systems. It is described how impulse responses of prevalent inspection set-ups can be modelled for efficient image enhancement. Common criteria for deconvolution performance measurements are listed and the results of a competitive benchmark of 13 state-of-the-art non-blind deconvolution algorithms are presented. Covered topics are illustrated by the example of a real-world inspection system for automatic quality control in woven fabrics. To meet real-time requirements, the efficient implementation of two selected algorithms based on GPU hardware is presented.
Year
DOI
Venue
2013
10.1109/I2MTC.2013.6555693
Instrumentation and Measurement Technology Conference
Keywords
Field
DocType
deconvolution,image enhancement,image restoration,gpu hardware,automated visual inspection systems,deconvolution performance measurements,digital images,image de-blurring methods,nonblind deconvolution algorithms,sample driven comparison,inspection,visualization,real time systems
Computer vision,Visual inspection,Blind deconvolution,Computer science,Deconvolution,Algorithm,Motion blur,Digital image,Artificial intelligence,Common Criteria,Image restoration,Benchmarking
Conference
ISSN
ISBN
Citations 
1091-5281
978-1-4673-4621-4
2
PageRank 
References 
Authors
0.41
8
4
Name
Order
Citations
PageRank
Dorian Schneider161.53
van Ekeris, T.220.41
Jacobsmuehlen, J.Z.320.41
Sebastian Gross413114.59