Fast reconstruction of hyperspectral images from coded acquisitions using a separability assumption: erratum.

We present an erratum to our publication [Opt. Express30(5), 8174 (2022)10.1364/OE.448893] correcting a numerical value without affecting the results and conclusions of the original publication.

Fast reconstruction of hyperspectral images from coded acquisitions using a separability assumption.

We present a fast reconstruction algorithm for hyperspectral images, utilizing a small amount of data without the need for any training. The method is implemented with a dual disperser hyperspectral imager and makes use of spatial-spectral correlations by a so-called separability assumption that assumes that the image is made of regions of homogenous spectra. The reconstruction algorithm is simple and ready-to-use and does not require any prior knowledge of the scene. A simple proof-of-principle experiment is performed, demonstrating that only a small number of acquisitions are required, and the resulting compressed data-cube is reconstructed near instantaneously.

Optimized coded aperture for frugal hyperspectral image recovery using a dual-disperser system.

We present a novel acquisition scheme based on a dual-disperser architecture, which can reconstruct a hyperspectral datacube using many times fewer acquisitions than spectral bands. The reconstruction algorithm follows a quadratic regularization approach, based on the assumption that adjacent pixels in the scene share similar spectra, and, if they do not, this corresponds to an edge that is detectable on the panchromatic image. A digital micro-mirror device applies reconfigurable spectral-spatial filtering to the scene for each acquisition, and the filtering code is optimized considering the physical properties of the system. The algorithm is tested on simple multi-spectral scenes with 110 wavelength bands and is able to accurately reconstruct the hyperspectral datacube using only 10 acquisitions.