|Title||Generalized probabilistic scale space for image restoration|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Wong, A., and A. Mishra|
|Journal||IEEE Transactions on Image Processing|
|Pagination||2774 - 2780|
|Keywords||2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography|
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-to-noise ratios and image degradation.