filtering by repeated integration
Many applications of digital filtering require a space variant filter - one whose shape or size varies with position. The usual algorithm for such filters, direct convolution, is very costly for wide kernels. Image prefiltering provides an efficient alternative. We explore one prefiltering technique, repeated integration , which is a generalization of Crow's summed area table .We find that convolution of a signal with any piecewise polynomial kernel of degree n --1 can be computed by integrating the signal n times and point sampling it several times for each output sample. The use of second or higher order integration permits relatively high quality filtering. The advantage over direct convolution is that the cost of repeated integration filtering does not increase with filter width. Generalization to two-dimensional image filtering is straightforward. Implementations of the simple technique are presented in both preprocessing and stream processing styles.
filtering integrability applications digitize space shape size posit algorithm direct cost kernel image efficiency exploration general signaling polynomial point sampling use order quality implementation stream processing