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Appl. Sci. 2017, 7(6), 526; doi:10.3390/app7060526

Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks

1
Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China
2
Intelligent Household Appliances Engineering Center, Zhejiang Business Technology Institute, Ningbo 315012, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Martin Richardson
Received: 6 March 2017 / Revised: 10 May 2017 / Accepted: 12 May 2017 / Published: 26 May 2017
(This article belongs to the Special Issue Holography and 3D Imaging: Tomorrows Ultimate Experience)
View Full-Text   |   Download PDF [4463 KB, uploaded 19 June 2017]   |  

Abstract

Super-resolution (SR) plays an important role in the processing and display of mixed-resolution (MR) stereoscopic images. Therefore, a stereoscopic image SR method based on view incorporation and convolutional neural networks (CNN) is proposed. For a given MR stereoscopic image, the left view of which is observed in full resolution, while the right view is viewed in low resolution, the SR method is implemented in two stages. In the first stage, a view difference image is defined to represent the correlation between views. It is estimated by using the full-resolution left view and the interpolated right view as input to the modified CNN. Accordingly, a high-precision view difference image is obtained. In the second stage, to incorporate the estimated right view in the first stage, a global reconstruction constraint is presented to make the estimated right view consistent with the low-resolution right view in terms of the MR stereoscopic image observation model. Experimental results demonstrated that, compared with the SR convolutional neural network (SRCNN) method and depth map based SR method, the proposed method improved the reconstructed right view quality by 0.54 dB and 1.14 dB, respectively, in the Peak Signal to Noise Ratio (PSNR), and subjective evaluation also implied that the proposed method produced better reconstructed stereoscopic images. View Full-Text
Keywords: stereoscopic imaging and coding; mixed-resolution stereoscopic image; super-resolution; view difference; convolutional neural networks stereoscopic imaging and coding; mixed-resolution stereoscopic image; super-resolution; view difference; convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pan, Z.; Jiang, G.; Jiang, H.; Yu, M.; Chen, F.; Zhang, Q. Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks. Appl. Sci. 2017, 7, 526.

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