# Local Fourier Slice Photography

## TL;DR

This paper introduces local Fourier slice photography, enabling refocused image reconstruction directly from sparse wavelet representations of light fields, significantly reducing storage and computation costs while maintaining image quality.

## Contribution

It presents a novel wavelet-based method that allows exact refocused image reconstruction from sparse light field data without dense pixel reconstruction.

## Key findings

- Reduces storage requirements for light field images
- Enables fast, exact refocused image reconstruction from sparse data
- Scales linearly with visual complexity and region size

## Abstract

Light field cameras provide intriguing possibilities, such as post-capture refocus or the ability to synthesize images from novel viewpoints. This comes, however, at the price of significant storage requirements. Compression techniques can be used to reduce these but refocusing and reconstruction require so far again a dense pixel representation. To avoid this, we introduce local Fourier slice photography that allows for refocused image reconstruction directly from a sparse wavelet representation of a light field, either to obtain an image or a compressed representation of it. The result is made possible by wavelets that respect the "slicing's" intrinsic structure and enable us to derive exact reconstruction filters for the refocused image in closed form. Image reconstruction then amounts to applying these filters to the light field's wavelet coefficients, and hence no reconstruction of a dense pixel representation is required. We demonstrate that this substantially reduces storage requirements and also computation times. We furthermore analyze the computational complexity of our algorithm and show that it scales linearly with the size of the reconstructed region and the non-negligible wavelet coefficients, i.e. with the visual complexity.