Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear and Nonlinear Image Deblurring: A Documented Study
SIAM Journal on Numerical Analysis
Selection of Optimal Stopping Time for Nonlinear Diffusion Filtering
International Journal of Computer Vision
Information measures in scale-spaces
IEEE Transactions on Information Theory
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Estimation of optimal PDE-based denoising in the SNR sense
IEEE Transactions on Image Processing
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Iterative smoothing algorithms are frequently applied in image restoration tasks. The result depends crucially on the optimal stopping (scale selection) criteria. An attempt is made towards the unification of the two frequently applied model selection ideas: (i) the earliest time when the 'entropy of the signal' reaches its steady state, suggested by J. Sporring and J. Weickert (1999), and (ii) the time of the minimal 'correlation' between the diffusion outcome and the noise estimate, investigated by P. Mrázek and M. Navara (2003). It is shown that both ideas are particular cases of the marginal likelihood inference. Better entropy measures are discovered and their connection to the generalized signal-to-noise ratio is emphasized.