Quaternion Convolutional Neural Networks for Heterogeneous Image Processing
Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edges or shapes, making it efficient with small or heterogeneous datasets. Quaternion-valued convolutional neural networks (QCNN) solved this problematic by introducing multidimensional algebra to CNN. This paper proposes to explore the fundamental reason of the success of QCNN over CNN, by investigating the impact of the Hamilton product on a color image reconstruction task performed from a gray-scale only training. By learning independently both internal and external relations and with less parameters than real valued convolutional encoder-decoder (CAE), quaternion convolutional encoder-decoders (QCAE) perfectly reconstructed unseen color images while CAE produced worst and gray-scale versions.
Code (1)
Tasks
DecoderImage ReconstructionSimilar Papers 제목 키워드 기반
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting
The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than separate color space componen…
Image InpaintingMatrix CompletionQuaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity. One of the models that perfectly suits with quaternion-valued data processing is…
Event DetectionSound Event DetectionHolistic Processing of Colour Images Using Novel Quaternion-Valued Wavelets on the Plane
Recently, novel quaternion-valued wavelets on the plane were constructed using an optimisation approach. These wavelets are compactly supported, smooth, orthonormal, non-separable and truly quaternionic. However, they ha…
DenoisingQuaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis
The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies rev…
Quaternion Convolutional Neural Networks
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 t…
Denoisingimage-classificationImage Classification