NDLI: Learning visual co-occurrence with auto-encoder for image super-resolution

Content Provider	IEEE Xplore Digital Library
Author	Yudong Liang Jinjun Wang Shizhou Zhang Yihong Gong
Copyright Year	2014
Description	Author affiliation: Inst. of Artificial Intell. & Robot., Xi'an Jiaotong Univ., Xi'an, China (Yudong Liang; Jinjun Wang; Shizhou Zhang; Yihong Gong)
Abstract	This paper proposes a novel neural network learning the essential mapping function between the low resolution and high resolution image for Image superresolution problem. In our approach, patch recurrence property of small patches in natural image are utilized as a prior to train the network. An autoencoder neutral network is designed to reconstruct the high resolution patches. The constraint that the output of the coding part should be similar as the corresponding high resolution patches is imposed to ameliorate the illness nature of the superresolution problem. In fact, the degeneration mapping from the high resolution image to the low resolution image is also integrated in the network. Both visual improvements and objective assessments are demonstrated on true images.
Starting Page	1
Ending Page	4
File Size	262948
Page Count	4
File Format	PDF
ISBN	9786163618238
DOI	10.1109/APSIPA.2014.7041671
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	Cambodia
Access Restriction	Subscribed
Rights Holder	Asia-Pacific Signal and Information Processing Ass
Subject Keyword	Training Computer vision Image resolution PSNR Conferences Signal resolution Biological neural networks
Content Type	Text
Resource Type	Article

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