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Fast Light Field Angular Resolution Enhancement Using Convolutional Neural Network
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DOI：10.1109/ACCESS.2021.3057401.png)
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摘要
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Current light field angular super-resolution algorithm based on deep learning has excessive computation cost and low operational efficiency, for sequentially up-sampling on each lenslet region of the light field image. In this paper, we propose a novel convolutional neural network to fastly enhance the angular resolution, via wholesale up-sampling lenslet regions. Firstly, the network simultaneously extracts the angular information of all lenslet regions on the input light field image. Then, from the extracted angular information, four feature maps are predicted. Especially, the angular resolution of each feature map is the same as that of the input light field image. Finally, to enhance the angular resolution, we integrate four feature maps into one image, by referring to angular information arrangement in lenslet regions. The experimental results verify the effectiveness of our proposed method. We only need 11.95s to enhance(actually double) the angular resolution of one light field image with 2562 x 3724 pixels, which surpasses 20 times faster than the state-of-the-art method. Meanwhile, our method also achieves average PSNR gains of 0.39 dB.
Keyword:
Light-field (LF)
angular super-resolution
convolutional neural network (CNN)
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3.6
论文数:
9.8W
被引数:
29.4W
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