Layered Light Field Reconstruction for Defocus Blur

ACM Trans. Graph. Pub Date : 2015-03-02 DOI:10.1145/2699647
K. Vaidyanathan, Jacob Munkberg, Petrik Clarberg, Marco Salvi
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引用次数: 31

Abstract

We present a novel algorithm for reconstructing high-quality defocus blur from a sparsely sampled light field. Our algorithm builds upon recent developments in the area of sheared reconstruction filters and significantly improves reconstruction quality and performance. While previous filtering techniques can be ineffective in regions with complex occlusion, our algorithm handles such scenarios well by partitioning the input samples into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. We also introduce a new separable formulation of sheared reconstruction filters that achieves real-time preformance on a modern GPU and is more than two orders of magnitude faster than previously published techniques.
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分层光场重建的散焦模糊
提出了一种从稀疏采样光场中重建高质量散焦模糊的新算法。我们的算法建立在剪切重建滤波器领域的最新发展基础上,显著提高了重建质量和性能。虽然以前的滤波技术在复杂遮挡的区域可能无效,但我们的算法通过将输入样本划分为深度层来很好地处理这种情况。这些深度层被独立过滤,然后结合在一起,考虑到层间的可见性。我们还介绍了一种新的剪切重建滤波器的可分离公式,该公式在现代GPU上实现了实时性能,并且比以前发表的技术快两个数量级。
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