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

• URL: http://arxiv.org/abs/2608.23175v1
• Date: Mon, 24 Aug 2026 12:23:27 GMT
• Title: Neighbor-Aware View Synthesis for Restoring Missing Views in Light-Field Camera Arrays
• Abstract summary: We propose a novel generative framework that synthesizes the absent views by exploiting information from a carefully selected subset of neighboring cameras.<n>Experiments on synthetic and real-world LF datasets demonstrate that our method produces visually plausible and photometrically accurate reconstructions.
• Score: 3.4239565380045343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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