PARAMETER-EFFICIENT AND RESOLUTION-ROBUST NETWORK ARCHITECTURES FOR IMAGE-TO-IMAGE TRANSLATION

PARAMETER-EFFICIENT AND RESOLUTION-ROBUST NETWORK ARCHITECTURES FOR IMAGE-TO-IMAGE TRANSLATION

One embodiment provides a method of using a computing device for image-to-image translation including accessing an image file containing a first amount of data. The computing device inputs the image file into a convolutional neural network (CNN). The CNN includes multiple Fourier layers. Each Fourie...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Wang, Hongzhi, Wong, Chun Lok, Klein, Levente, Syeda-Mahmood, Tanveer F, Silva, Ademir Ferreira da, Singh, Jitendra
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:One embodiment provides a method of using a computing device for image-to-image translation including accessing an image file containing a first amount of data. The computing device inputs the image file into a convolutional neural network (CNN). The CNN includes multiple Fourier layers. Each Fourier layer includes a Fourier transform, a linear feature transformation in a frequency domain and an inverse Fourier transform. Each linear feature transformation in the frequency domain is shared by different frequency components to reduce a number of parameters. The CNN outputs an output image file that includes contents that are translated from the input image file.