Complex Valued Convolutional Neural Network

Complex-valued convolutional neural networks (CV-CNNs) leverage the richer representational capacity of complex numbers to improve upon traditional real-valued CNNs, primarily aiming to enhance feature extraction and classification accuracy across diverse applications. Current research focuses on developing and analyzing CV-CNN architectures, including those incorporating complex convolutions, matched filters (like widely linear matched filters), and attention mechanisms, often applied to data with inherent complex structure like radar images, hyperspectral data, and MRI scans. This research demonstrates improved performance in various fields, including image processing, signal classification, and biometric identification, highlighting the potential of CV-CNNs to surpass real-valued counterparts in tasks where phase information is crucial.