A new heuristic filter based on the optimum filter for disjoint noise developed by Javidi and Wang [J. Opt. Soc. Am. A <b>11</b>, 2604 (1995)] is presented. In this new filter a number of optimum filters built from single training images are combined linearly by use of the synthetic discriminant function (SDF) approach into a distortion-invariant filter for disjoint noise. Like the traditional SDF approach, this summation technique makes it possible to control the height of the correlation peak easily, for example, if a uniform filter response is needed. The filter is compared with the distortion-invariant version of the optimum filter on images with low contrast and high levels of nonoverlapping clutter. The new filter shows good results, demonstrating that it is, with very simple heuristic methods, possible to improve the performance of distortion-invariant filters for nonoverlapping noise.
© 1998 Optical Society of America
(070.5010) Fourier optics and signal processing : Pattern recognition
(100.5760) Image processing : Rotation-invariant pattern recognition
(100.6740) Image processing : Synthetic discrimination functions
Henrik Sjöberg and Bertrand Noharet, "Distortion-Invariant Filter for Nonoverlapping Noise," Appl. Opt. 37, 6922-6930 (1998)