Clonal selection algorithm based cloning template learning for edge detection in digital images with CNN

A cellular neural network (CNN) based edge detector optimized by clonal selection algorithm is presented. Cloning templates of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing edge detectors and offers superior performance in edge detection in digital images.