CutMAA: Motion-Aware Data Augmentation for Light Field Super-Resolution

Sojin Yun, Namhyuk Ahn, In Kyu Park

Abstract: Light field super-resolution (SR) is a task that aims to enhance the spatial resolution of light field images by utilizing information from multiple sub-aperture images (SAIs). While deep learning-based methods have demonstrated impressive performance, their application is often hindered by the lack of sufficient training data. To address this challenge, we propose CutMAA, a motion-aware data augmentation (DA) specialized for light field SR. Existing DA methods for light field SR do not consider the spatial-angular correlation inherent in light fields. By contrast, CutMAA leverages motion information to effectively incorporate such correlation. CutMAA calculates the motion difference between the central SAI and others, performing a warping process to align the pixel positions of each SAI accordingly, resulting in warped SAIs. From these warped SAIs, patches are extracted, blended, and then pasted into the light field at other resolutions. Compared to the previous DAs, our method significantly enhances the light field SR performance. Since CutMAA can be seamlessly integrated into existing frameworks, this ensures broad applicability across various light field SR scenarios.

External IDs:dblp:journals/access/YunAP25