IEST: Interpolation-Enhanced Shearlet Transform for Light Field Reconstruction Using Adaptive Separable Convolution
IEST: Interpolation-Enhanced Shearlet Transform for Light Field Reconstruction Using Adaptive Separable Convolution
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DOI：
10.23919/eusipco.2019.8903168
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Yuan Gao;R. Koch;R. Bregović;A. Gotchev
中科院分区：
文献类型：
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作者：
Yuan Gao;R. Koch;R. Bregović;A. Gotchev
光场重建算法的性能通常受到输入稀疏采样光场（SSLF）视差范围的影响。本文发现（i）最先进的视频帧插值方法之一，即自适应可分离卷积（SepConv），对于小视差范围（20 像素）的 SSLF 上的光场重建特别有效。因此，为了充分利用这两种方法来解决具有中等和大视差范围的 SSLF 上具有挑战性的光场重建问题，通过以从粗到细的方式结合这两种方法，提出了一种称为插值增强剪切波变换（IEST）的新方法。具体来说，采用ST对目标光场进行粗略估计，然后通过SepConv进行细化，以提高涉及小视差范围的视差视图的重建质量。实验结果表明，在具有中等和大视差范围的不同现实场景的九个具有挑战性的水平视差评估 SSLF 数据集上，IEST 优于其他最先进的光场重建方法。
The performance of a light field reconstruction algorithm is typically affected by the disparity range of the input Sparsely-Sampled Light Field (SSLF). This paper finds that (i) one of the state-of-the-art video frame interpolation methods, i.e. adaptive Separable Convolution (SepConv), is especially effective for the light field reconstruction on a SSLF with a small disparity range ( 20 pixels). Therefore, to make full use of both methods to solve the challenging light field reconstruction problem on SSLFs with moderate and large disparity ranges, a novel method, referred to as Interpolation-Enhanced Shearlet Transform (IEST), is proposed by incorporating these two approaches in a coarse-to-fine manner. Specifically, ST is employed to give a coarse estimation for the target light field, which is then refined by SepConv to improve the reconstruction quality of parallax views involving small disparity ranges. Experimental results show that IEST outperforms the other state-of-the-art light field reconstruction methods on nine challenging horizontalparallax evaluation SSLF datasets of different real-world scenes with moderate and large disparity ranges.