# A learning-based view extrapolation method for axial super-resolution

arXiv: https://arxiv.org/abs/2103.06510 · HTML: https://mlsanity.com/paper/2103.06510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Pushing the Boundaries of View Extrapolation with Multiplane Images](https://mlsanity.com/paper/1905.00413/) (2019)
- [Learning-Based View Synthesis for Light Field Cameras](https://mlsanity.com/paper/1609.02974/) (2016)
- [Light Field Reconstruction Using Shearlet Transform](https://mlsanity.com/paper/1509.08969/) (2015)
- [A Fourier Disparity Layer representation for Light Fields](https://mlsanity.com/paper/1901.06919/) (2019)
- [Simultaneous whole-animal 3D-imaging of neuronal activity using light field microscopy](https://mlsanity.com/paper/1401.5333/) (2014)
- [Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks](https://mlsanity.com/paper/1801.04314/) (2018)

## Abstract (arXiv)

Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. High refocusing precision can be essential for some light field applications like microscopy. In this paper, we propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images (EPIs). As extended numerical aperture (NA) in classical imaging, the extrapolated light field gives re-focused images with a shallower depth of field (DOF), leading to more accurate refocusing results. Most importantly, the proposed approach does not need accurate depth estimation. Experimental results with both synthetic and real light fields show that the method not only works well for light fields with small baselines as those captured by plenoptic cameras (especially for the plenoptic 1.0 cameras), but also applies to light fields with larger baselines.