Edge detection based on decision-level information fusion and its application in hybrid image filtering

A new edge detection method, based on decision-level information fusion, is proposed to classify image pixels into edge and non-edge categories. Traditional edge detection algorithms make the detection decision under a single criterion, which may perform inefficiently with a change of noise model. We use fusion entropy as a criterion to integrate decisions from different classifiers in order to improve the edge detection accuracy. The proposed decision fusion based edge detection method is applied to image filtering and leads to a weighted hybrid-filtering algorithm. Simulation results show that the new edge detection method has better performance than the single criterion edge detection methods.