用于恢复光场相机阵列中缺失视图的邻域感知视图合成

中文摘要 AI 辅助

在光场（LF）成像系统中，相机阵列的密集空间采样支持强大的捕获后功能，如重聚焦和深度估计。然而，实际光场捕获常受硬件故障影响，阵列中一台或多台相机失效，导致子孔径图像缺失，重建质量下降。本文研究光场相机阵列中缺陷或缺失视图的恢复问题，提出一种新型生成框架，通过利用精心选择的邻域相机子集信息合成缺失视图。这些选定图像连同指示其位置和目标视图的位置编码图，被输入到条件生成对抗网络（cGAN）中，该网络经训练可生成几何一致的缺失视点。在合成和真实光场数据集上的大量实验表明，本文方法生成的重建结果视觉上合理、光度学准确，在视图插值任务上的定量和定性表现均优于基线方法，该框架为容错光场图像采集提供了一种鲁棒且高效的解决方案。

英文摘要

In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth estimation. However, real-world LF capture is often affected by hardware malfunctions, where one or more cameras in the array fail, leading to missing sub-aperture images and degraded reconstruction quality. This paper addresses the problem of defective or missing view restoration in light-field camera arrays. We propose a novel generative framework that synthesizes the absent views by exploiting information from a carefully selected subset of neighboring cameras. These selected images, along with a positional encoding map indicating both their locations and the desired target view, are fed into a conditional Generative Adversarial Network (cGAN) trained to generate the missing viewpoint in a geometrically consistent manner. Extensive experiments on synthetic and real-world LF datasets demonstrate that our method produces visually plausible and photometrically accurate reconstructions, outperforming baselines for view interpolation both quantitatively and qualitatively. The proposed framework thus offers a robust and efficient solution for fault-tolerant light-field image acquisition.

发表机构

• Indian Institute of Technology Kanpur (IIT Kanpur)（印度坎普尔理工学院（IIT坎普尔））

机构由 AI 辅助整理，请以论文原文为准。