Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios. To address this problem, we introduce LytroZoom, the first real-world light field SR dataset capturing paired low- and high-resolution light fields of diverse indoor and outdoor scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency Projection Network (OFPNet), which decomposes the omni-frequency components and iteratively enhances them through frequency projection operations to address spatially variant degradation processes present in all frequency components. Experiments demonstrate that models trained on LytroZoom outperform those trained on synthetic datasets and are generalizable to diverse content and devices. Quantitative and qualitative evaluations verify the superiority of OFPNet. We believe this work will inspire future research in real-world light field SR.

本文介绍了使用 LYTRO ILLUM 相机的 LytroZoom 数据集以及引入 Omni-Frequency Projection Network(OFPNet) 模型进行深度学习的实时光场图像超分辨率提升(SR)，实验证明模型在真实世界的场景下比合成数据集训练模型表现更好，而且对一些频率变化的 degradation 过程同样有良好的表现。

走向真实世界光场超分辨率