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Stereo Super-Resolution via a Deep Convolutional Network

Li, Junxuan; You, Shaodi; Robles-Kelly, Antonio

Description

In this paper, we present a method for stereo super-resolution which employs a deep network. The network is trained using the residual image so as to obtain a high resolution image from two, low resolution views. Our network is comprised by two deep sub-nets which share, at their output, a single convolutional layer. This last layer in the network delivers an estimate of the residual image which is then used, in combination with the left input frame of the stereo pair, to compute the...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
URI: http://hdl.handle.net/1885/205482
Source: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications
DOI: 10.1109/DICTA.2017.8227492

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