| 摘要 |
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. |
