New binary image filtering method based on a modified perceptron training algorithm
Octavian V. Sarca, Jaakko T. Astola · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
The proposed filter consists of a set of linear separable Boolean functions (LSBF) from which, each time, only one is selected to perform the actual filtering. The selection depends on the pixel values in a subset V of the filter window W. The paper shows that in terms of performance, the introduced filter is bounded between the V-windowed Boolean filter and the W-windowed Boolean filter. The advantage of the proposed filter is that it can be used with very large windows which permits to overcome the limits of the other binary image filtering methods. The paper proves that the new filter can be designed by training each LSBF independently. SEveral design methods for LSBF are analyzed and a new efficient and reliable training algorithm is developed. The experimental results compare the performance of the new filter against the Boolean filter and LSBF for various windows. The new LSBF training algorithm is also compared with the design method used by Lee and Lee for linear separable threshold Boolean filters.