# Light Field Reconstruction Using Shearlet Transform

arXiv: https://arxiv.org/abs/1509.08969 · HTML: https://mlsanity.com/paper/1509.08969/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2015.

## Built on by (most cited first, 19 of 19)

- [Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering](https://mlsanity.com/paper/2106.02634/) (2021)
- [Learning Neural Light Fields with Ray-Space Embedding Networks](https://mlsanity.com/paper/2112.01523/) (2021)
- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network](https://mlsanity.com/paper/2104.06797/) (2021)
- [Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines](https://mlsanity.com/paper/2108.07408/) (2021)
- [Light Field Implicit Representation for Flexible Resolution Reconstruction](https://mlsanity.com/paper/2112.00185/) (2021)
- [Content-aware Warping for View Synthesis](https://mlsanity.com/paper/2201.09023/) (2022)
- [Model-inspired Deep Learning for Light-Field Microscopy with Application to Neuron Localization](https://mlsanity.com/paper/2103.06164/) (2021)
- [A Hierarchical Coding Scheme for Glasses-free 3D Displays Based on Scalable Hybrid Layered Representation of Real-World Light Fields](https://mlsanity.com/paper/2104.09378/) (2021)
- [Scale-Consistent Fusion: from Heterogeneous Local Sampling to Global Immersive Rendering](https://mlsanity.com/paper/2106.09548/) (2021)
- [A Novel Hierarchical Light Field Coding Scheme Based on Hybrid Stacked Multiplicative Layers and Fourier Disparity Layers for Glasses-Free 3D Displays](https://mlsanity.com/paper/2108.12399/) (2021)
- [Geo-NI: Geometry-aware Neural Interpolation for Light Field Rendering](https://mlsanity.com/paper/2206.09736/) (2022)
- [Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum](https://mlsanity.com/paper/2101.09420/) (2021)
- [A learning-based view extrapolation method for axial super-resolution](https://mlsanity.com/paper/2103.06510/) (2021)
- [Spectral analysis of re-parameterized light fields](https://mlsanity.com/paper/2110.06064/) (2021)
- [Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction](https://mlsanity.com/paper/2204.00193/) (2022)
- [ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided Sampling](https://mlsanity.com/paper/2204.03476/) (2022)
- [A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays](https://mlsanity.com/paper/2210.01447/) (2022)
- [Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution](https://mlsanity.com/paper/2305.20006/) (2023)

## Abstract (arXiv)

In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images in a directionally sensitive transform domain, obtained by an adapted discrete shearlet transform. The used iterative thresholding algorithm provides high-quality reconstruction results for relatively big disparities between neighboring views. The generated densely sampled light field of a given 3D scene is thus suitable for all applications which requires light field reconstruction. The proposed algorithm is compared favorably against state of the art depth image based rendering techniques.