实现稀疏与密集光场任意区域同步聚焦，提升成像质量。
Arbitrary Volumetric Refocusing of Dense and Sparse Light Fields
- 采用像素依赖的移位-求和方法，独立聚焦每个像素。
- 稀疏光场仅用20%数据，结构相似性超0.9，近乎无伪影。
- 适合需要多区域灵活聚焦的3D成像场景。
四维光场（LF）相比二维图像，不仅能捕捉场景纹理信息，还能记录几何信息。后捕获聚焦是利用几何信息的重要应用。以往方法通常仅能对单一平面或体域聚焦，无法同时生成同深度范围内的清晰与模糊区域。本文提出端到端流程，可同时对密集或稀疏光场中的多个任意平面或体域进行聚焦。采用像素依赖的移位-求和方法，使每个像素独立聚焦。针对稀疏光场因空间欠采样导致的鬼影伪影，引入基于U-Net的深度学习模型，几乎完全消除伪影。在多个光场数据集上的实验验证了该方法的有效性。特别是，仅使用20%数据的稀疏光场经本方法聚焦后，结构相似性指数超过0.9。
原文摘要 · Abstract (English)
A four-dimensional light field (LF) captures both textural and geometrical information of a scene in contrast to a two-dimensional image that captures only the textural information of a scene. Post-capture refocusing is an exciting application of LFs enabled by the geometric information captured. Previously proposed LF refocusing methods are mostly limited to the refocusing of single planar or volumetric region of a scene corresponding to a depth range and cannot simultaneously generate in-focus and out-of-focus regions having the same depth range. In this paper, we propose an end-to-end pipeline to simultaneously refocus multiple arbitrary planar or volumetric regions of a dense or a sparse LF. We employ pixel-dependent shifts with the typical shift-and-sum method to refocus an LF. The pixel-dependent shifts enables to refocus each pixel of an LF independently. For sparse LFs, the shift-and-sum method introduces ghosting artifacts due to the spatial undersampling. We employ a deep learning model based on U-Net architecture to almost completely eliminate the ghosting artifacts. The experimental results obtained with several LF datasets confirm the effectiveness of the proposed method. In particular, sparse LFs refocused with the proposed method archive structural similarity index higher than 0.9 despite having only 20% of data compared to dense LFs.
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