Multidimensional Compressed Sensing for Spectral Light Field Imaging

TL;DR

This paper introduces a 5D compressed sensing framework for spectral light field imaging that enables faster reconstruction and reduced memory usage compared to traditional 1D models, advancing multi-spectral data acquisition.

Contribution

It proposes a novel 5D measurement model for spectral light fields, matching their intrinsic dimensionality, and demonstrates its efficiency over existing 1D vectorized approaches.

Findings

5D model achieves significantly faster reconstruction times.

The 5D approach requires less memory than 1D models.

Mathematical and empirical validation of 5D and 1D model equivalence.

Abstract

This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectralcoded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor. We propose a model that employs compressed sensing techniques to reconstruct the complete multi-spectral light field from undersampled measurements. Unlike previous work where a light field is vectorized to a 1D signal, our method employs a 5D basis and a novel 5D measurement model, hence, matching the intrinsic dimensionality of multispectral light fields. We mathematically and empirically show the equivalence of 5D and 1D sensing models, and most importantly that the 5D framework achieves orders of magnitude faster reconstruction while requiring a small fraction of the memory. Moreover, our new multidimensional sensing model opens new research directions for designing efficient visual data acquisition algorithms and hardware.