Dictionary design and disparity interpolation on distributed compressed sensing for light field image
Dictionary design and disparity interpolation on distributed compressed sensing for light field image
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DOI：
10.1109/apsipa.2017.8282035
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发表时间：
2017-12
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影响因子：
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通讯作者：
Yusaku Akiyoshi;T. Sumi;Y. Kuroki
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文献类型：
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作者：
Yusaku Akiyoshi;T. Sumi;Y. Kuroki
本文讨论了光场相机产生的多视点图像的分布式压缩感知的两个术语。我们首先讨论了用ADMM（交替方向乘法）设计的字典和K-SVD的性能，因为重建图像的质量取决于字典。第二个讨论是非关键帧的视差插值。插值精度有助于重建图像的质量和有效的字典设计。然后，本文比较了三种视差插值方法：重叠块匹配法、传统光流法和TVL 1光流法。实验结果表明，采用ADMM的字典设计速度比K-SVD快，而PSNR值略低于K-SVD，且TVL 1-光流最快保持最高PSNR值。
This paper discusses two terms of distributed compressed sensing for multi-view point images generated with a light field camera. Our first discussion is on the performance of dictionary designed with ADMM (Alternating Direction Method of Multipliers) and that of K-SVD since reconstructed image quality depends on the dictionary. The second discussion is disparity interpolation of non-key frames. The interpolation accuracy contributes to reconstructed image quality and effective dictionary design. Then, this paper compares three disparity interpolation methods: the overlapped block matching, the conventional optical flow, and the TVL1-optical-flow. Experimental results show that the dictionary design with ADMM is faster than that with K-SVD whereas PSNR values using ADMM are slightly lower than those of K-SVD, and the TVL1-optical flow is the fastest holding the highest PSNR values.