A Study on Light Field Denoising for 3D Consistent Visualization
A Study on Light Field Denoising for 3D Consistent Visualization
复制标题
3D一致性可视化光场去噪研究
DOI：
10.1109/icip40778.2020.9191291
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Takayuki Hamamoto
中科院分区：
文献类型：
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作者：
Shunsuke Ishihara;Kazuya Kodama;Takayuki Hamamoto
提出了一种新的三维一致性可视化光场去噪方法。光场数据通常被由于在不充足的照明下缺乏曝光而导致的可怕的噪声显著地降级，特别是如果通过透镜阵列获取。然后，最近的研究注意到，4D光场对应于根据3D场景彼此良好相关的结构化多视图图像。例如，基于CNN的去噪方法通过分析4D光场的各种子空间来利用相关性。本文提出了一种基于全变分最小化的简单图像复原方法，对多视点图像中提取的对应像素点组成的小图像进行4D光场去噪。我们使用合成和真实的图像的实验结果，以澄清我们所提出的方法如何有效地与其他方法相比，三维一致的可视化。此外，我们讨论了未来可能的改进所提出的方法时，将我们的新方法与传统的2D图像去噪更有效的光场去噪。
We propose a novel method of light field denoising for 3D consistent visualization. Light field data are often significantly degraded by awful noise due to lack of exposure under insufficient lighting, especially, if acquired by a lens array. Then, recent studies notice that a 4D light field corresponds to structured multi-view images that are correlated to each other well according to 3D scenes. For example, a CNN-based denoising method utilizes the correlation by analyzing various subspaces of 4D light fields. In this paper, 4D light field denoising is achieved by applying simple restoration based on Total Variation minimization to small images composed of corresponding pixels extracted from multi-view images. We show experimental results using synthetic and real images to clarify how our proposed method effectively works for 3D consistent visualization in comparison with the other methods. In addition, we discuss possible future improvement of the proposed method when integrating our novel approach with the conventional 2D image denoising for more effective light field denoising.