Method and System for Performing Robust Regular Gridded Data Resampling

During data resampling, bad samples are ignored or replaced with some combination of the good sample values in the neighborhood being processed. The sample replacement can be performed using a number of approaches, including serial and parallel implementations, such as branch-based implementations, matrix-based implementations, and function table-based implementations, and can use a number of modes, such as nearest neighbor, bilinear and cubic convolution.