Visible to the public Coupled Autoencoder Network with Joint Regularizations for Image Super-resolution

TitleCoupled Autoencoder Network with Joint Regularizations for Image Super-resolution
Publication TypeConference Paper
Year of Publication2016
AuthorsZheng, Huanhuan, Qu, Yanyun, Zeng, Kun
Conference NameProceedings of the International Conference on Internet Multimedia Computing and Service
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4850-8
Keywordsacoustic coupling, Coupled Autoencoder Network, Human Behavior, pubcrawl, Regularizations, Resiliency, scalabilty, Super-resolution
AbstractThis paper aims at building a sparse deep autoencoder network with joint regularizations for image super-resolution. A map is learned from the low-resolution feature space to high-resolution feature space. In the training stage, two autoencoder networks are built for image representation for low resolution images and their high resolution counterparts, respectively. A neural network is constructed to learn a map between the features of low resolution images and high resolution images. Furthermore, due to the local smoothness and the redundancy of an image, the joint variation regularizations are unified with the coupled autoencoder network (CAN). For the local smoothness, steerable kernel variation regularization is designed. For redundancy, non-local variation regularization is designed. The joint regularizations improve the quality of the super resolution image. Experimental results on Set5 demonstrate the effectiveness of our proposed method.
Citation Keyzheng_coupled_2016