Accelerating Existing Non-Blind Image Deblurring Techniques through a Strap-On Limited-Memory Switched Broyden Method

Ichraf LAHOULI  Robby HAELTERMAN  Joris DEGROOTE  Michal SHIMONI  Geert DE CUBBER  Rabah ATTIA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.5   pp.1288-1295
Publication Date: 2018/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017MVP0022
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision and its Applications)
Category: Machine Vision and its Applications
Keyword: 
image deblurring,  quasi-Newton,  limited-memory,  switched Broyden method,  

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Summary: 
Video surveillance from airborne platforms can suffer from many sources of blur, like vibration, low-end optics, uneven lighting conditions, etc. Many different algorithms have been developed in the past that aim to recover the deblurred image but often incur substantial CPU-time, which is not always available on-board. This paper shows how a “strap-on” quasi-Newton method can accelerate the convergence of existing iterative methods with little extra overhead while keeping the performance of the original algorithm, thus paving the way for (near) real-time applications using on-board processing.