Frank Geng

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The paper develops two-dimensional (2D), nonseparable, piecewise cubic convolution (PCC) for image interpolation. Traditionally, PCC has been implemented based on a one-dimensional (1D) derivation with a separable generalization to two dimensions. However, typical scenes and imaging systems are not separable, so the traditional approach is suboptimal. We(More)
This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]x[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.
This paper describes improved piecewise cubic convolution for two-dimensional image reconstruction. Piecewise cubic convolution is one of the most popular methods for image reconstruction, but the traditional approach uses a separable two-dimensional convolution kernel that is based on a one-dimensional derivation. The traditional approach is sub-optimal(More)
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