Abstract. Neural networks in the real domain have been studied for a
long time and achieved promising results in many vision tasks for recent
years. However, the extensions of the neural network models in other
number fields and their potential applications are not fully-investigated
yet. Focusing on color images, which can be naturally represented as
quaternion matrices, we propose a quaternion convolutional neural network (QCNN) model to obtain more representative features. In particular, we re-design the basic modules like convolution layer and fullyconnected layer in the quaternion domain, which can be used to establish fully-quaternion convolutional neural networks. Moreover, these
modules are compatible with almost all deep learning techniques and can
be plugged into traditional CNNs easily. We test our QCNN models in
both color image classification and denoising tasks. Experimental results
show that they outperform the real-valued CNNs with same structures