Compressive 4D Light Field Reconstruction Using Orthogonal Frequency Selection
Compressive 4D Light Field Reconstruction Using Orthogonal Frequency Selection
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DOI：
10.1109/icip.2018.8451110
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
F. Hawary;Guillaume Boisson;C. Guillemot;P. Guillotel
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文献类型：
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作者：
F. Hawary;Guillaume Boisson;C. Guillemot;P. Guillotel
我们提出了一种新的方法来重建一个4D光场从一组随机的测量。4D光场块可以由傅立叶域中的稀疏模型表示。因此，所提出的算法通过选择最适合可用样本的模型的频率来逐块重建光场，同时强制与近似残差正交。该方法在峰值信噪比（PSNR）方面实现了非常高的重建质量。在几个数据集上的实验表明，与最先进的算法相比，质量提高了1dB以上。
We present a new method for reconstructing a 4D light field from a random set of measurements. A 4D light field block can be represented by a sparse model in the Fourier domain. As such, the proposed algorithm reconstructs the light field, block by block, by selecting frequencies of the model that best fits the available samples, while enforcing orthogonality with the approximation residue. The method achieves a very high reconstruction quality, in terms of Peak Signal-to-Noise Ratio (PSNR). Experiments on several datasets show significant quality improvements of more than 1dB compared to state-of-the-art algorithms.