Compressive Spectral Light Field Image Reconstruction via Online Tensor Representation.

Abstract

In recent years there has been an increasing interest in sensing devices that capture multidimensional information such as the spectral light field (SLF) images, which are 5-dimensional (5D) representations of a scene including 2D spatial, 2D angular and 1D spectral information. Spatio-spectral and angular information plays an important role in modern applications spanning from microscopy to computer vision. However, SLF sensors use expensive beam-splitters or cameras arrays placed in tandem, which split the sensing problem in two time consuming and independent tasks: spectral and light field imaging tasks. This work proposes a compressive spectral light field imaging architecture that builds on the principles of the compressive imaging framework, to capture multiplexed representations of the multidimensional information, so that, less measurements are required to capture the SLF data cube. Alongside, we propose a computational algorithm to recover the 5D information from the compressed measurements, exploiting the inherent high correlations within the SLF by treating them as 3D tensors. Furthermore, exploiting the geometry properties of the proposed optical architecture, the Tucker decomposition is applied to the set of compressed measurements, so that, an ad-hoc dictionary-like image representation basis is calculated online. This in turn, entails a more accurate reconstruction of the SLF since the dictionary fits the specific characteristics of the image itself. We demonstrate through simulations over three SLF datasets captured in our laboratory, and an experimental proof-of-concept implementation, that the proposed compressive imaging device together with the proposed computational algorithm represent an efficient alternative to capture SLF, compared to conventional methods that employ either side-information or multiple sensors. Also, we show that the tensor-based proposed algorithm exhibits a lower computational complexity than the matrix-based state of the art counterparts, thus enabling fast processing of multidimensional images.