Transformations of Image Filters for Machine Vision Using Complex-Valued Neural Networks

Abstract

Neural networks expanded to complex domains have recently been studied in the field of computational intelligence. Complex-valued neural networks are effective for learning the relationships between complex inputs and outputs, and applications to complex analysis and complex image processing have been studied (Hirose, 2006). In addition, the effectiveness of the computational complexity and the number of training data has been confirmed when learning mappings in two-dimensional space (Nitta, 1997). Also, a method for complex-valued network inversion to produce an inverse mapping was proposed as a related technique using a complex-valued neural network (Ogawa, 2009). We can obtain forward mappings and inverse mappings in complex domains using these methods.