Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Reconstructing Surfaces by Volumetric Regularization Using Radial Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a novel method to approximate shift-variant Gaussian filtering of an image using a set of shift-invariant Gaussian filters. This approximation affords filtering of the image using fast convolution techniques that rely on the FFT, while achieving a result that closely matches the shift-variant result. We demonstrate the method in a CT colonography application that reduces the pseudoenhancement effect, which is a local brightening artifact in CT imaging that can result from the use of oral contrast agents. Experimental results demonstrate the effectiveness of the method and emphasize its computational efficiency.