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We introduce a new exponential-based shift-invariant approach to image interpolation using stochastic modeling. Our model stems from Sobolev reproducing kernels of exponential type, motivated by their role in continuous-domain stochastic autoregressive processes. An algorithm based on these tools is developed and tested. Experimental results of image and texture scaling show that these exponential...
A Sobolev reproducing-kernel Hilbert space approach to image interpolation is introduced. The underlying kernels are exponential functions and are related to stochastic autoregressive image modeling. The corresponding image interpolants can be implemented effectively using compactly-supported exponential B-splines. A tight l2 upper-bound on the interpolation error is then derived, suggesting that...
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