Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
Date
2016
Authors
Mao, X.
Shen, C.
Yang, Y.
Editors
Lee, D.D.
Sugiyama, M.
Luxburg, U.V.
Guyon, I.
Garnett, R.
Sugiyama, M.
Luxburg, U.V.
Guyon, I.
Garnett, R.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Advances in neural information processing systems, 2016 / Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (ed./s), vol.29, pp.2810-2818
Statement of Responsibility
Xiao-Jiao Maoy, Chunhua Shen, Yu-Bin Yang
Conference Name
Annual Conference on Neural Information Processing Systems (NIPS) (5 Dec 2016 - 10 Dec 2016 : Barcelona, Spain)
Abstract
In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and deconvolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. Deconvolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, the skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to deconvolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than recent state-of-the-art methods.
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© 2016 NIPS Foundation - All Rights Reserved.