Neighbor-Aware View Synthesis for Restoring Missing Views in Light-Field Camera Arrays

2026-08-24

AI Summary

本文针对光场相机阵列中因硬件故障导致的视图缺失问题，提出了一种利用邻近相机信息和条件生成对抗网络(cGAN)合成缺失视图的新方法。

Abstract

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.

Problem

• light-field imaging
• camera array
• missing views
• view restoration
• hardware malfunctions

Innovation

• Neighbor-Aware
• Generative Framework
• Conditional Generative Adversarial Network (cGAN)
• Light-Field Imaging
• View Synthesis