Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks
AbstractSuper-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
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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.
Pan Z, Jiang G, Jiang H, Yu M, Chen F, Zhang Q. Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks. Applied Sciences. 2017; 7(6):526.Chicago/Turabian Style
Pan, Zhiyong; Jiang, Gangyi; Jiang, Hao; Yu, Mei; Chen, Fen; Zhang, Qingbo. 2017. "Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks." Appl. Sci. 7, no. 6: 526.
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