Generalized probabilistic scale space for image restoration

Authors:
Alexander Wong;Akshaya K. Mishra
Affiliations:
University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada
Venue:
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Year:
2010

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Abstract

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-tonoise ratios and image degradation.