Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

In this Letter, we present a new snapshot hyperspectral (HS) camera based on a multi-aperture design. The technique uses an array of modified Fabry–Perot resonators together with a lens array in order to acquire an array of spectrally multiplexed modulated sub-images. Then the original HS image is reconstructed using a compressive sensing reconstruction algorithm. The HS camera has high optical throughput and enables acquisition of almost gigapixel HS datacubes with hundreds of spectral bands. Using our camera, we demonstrate optical compression of approximately 37:1.

Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods.

Several hyperspectral (HS) systems based on compressive sensing (CS) theory have been presented to capture HS images with high accuracy and with a lower number of measurements than needed by conventional systems. However, the reconstruction of HS compressed measurements is time-consuming and commonly involves hyperparameter tuning per each scenario. In this paper, we introduce a Convolutional Neural Network (CNN) designed for the reconstruction of HS cubes captured with CS imagers based on spectral modulation. Our Deep Neural Network (DNN), dubbed DeepCubeNet, provides significant reduction in the reconstruction time compared to classical iterative methods. The performance of DeepCubeNet is investigated on simulated data, and we demonstrate for the first time, to the best of our knowledge, real reconstruction of CS HS measurements using DNN. We demonstrate significantly enhanced reconstruction accuracy compared to iterative CS reconstruction, as well as improvement in reconstruction time by many orders of magnitude.

We present a new fast compressive spectroscopic technique based on the resonance spectrometric mechanism. This technique uses an appropriately designed Fabry–Perot resonator and a photo-sensor in order to acquire different multiplexed spectral modulations, from which the original signal is reconstructed using a compressive sensing reconstruction algorithm. We present experimental results that demonstrate the acquisition of hundreds of spectral bands with a compression ratio of about 1:13.

In various applications, such as remote sensing and quality inspection, hyperspectral (HS) imaging is performed by spatially scanning an object. In this work, we present a new compressive hyperspectral imaging method that performs along-track scanning. The method relies on the compressive sensing miniature ultra-spectral imaging (CS-MUSI) system, which uses a single liquid crystal (LC) cell for spectral encoding and provides a more efficient way of HS data acquisition, compared to classical spatial scanning based systems. The experimental results show that a compression ratio of about 1:10 can be reached. Owing to the inherent compression, the captured data is preprepared for efficient storage and transmission.