Abstract
An algorithm is developed for defining small kernels that are conditioned on the important components of the imaging process: the nature of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and post-filter interpolation. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernal values that minimize the expected mean-square error of the estimate of the scene characteristic. This development is consistent with the derivation of the spatially unconstrained Wiener characteristic filter, but leads to a small, spatially constrained convolution kernel. Simulation experiments demonstrate that the algorithm is more flexible than traditional small-kernel techniques and yields more accurate estimates.
Original language | English (US) |
---|---|
Pages (from-to) | 57-63 |
Number of pages | 7 |
Journal | Proceedings - International Conference on Pattern Recognition |
Volume | 2 |
State | Published - 1990 |
Event | Proceedings of the 10th International Conference on Pattern Recognition - Atlantic City, NJ, USA Duration: Jun 16 1990 → Jun 21 1990 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition