DeepCubeNet: reconstruction of spectrally compressive sensed hyperspectral images with deep neural networks.

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.

Fast Method for Liquid Crystal Cell Spatial Variations Estimation Based on Modeling the Spectral Transmission.

Liquid crystal phase retarders are utilized by photonic devices and imaging systems for various applications, such as tunable filtering, light modulation, polarimetric imaging, remote sensing and quality inspection. Due to technical difficulties in the manufacturing process, these phase retarders may suffer from spatial non-uniformities, which degrade the performance of the systems. These non-uniformities can be characterized by measuring the spectral transmission at each voltage and each point on the liquid crystal cell, which is time consuming. In this work, we present a new fast and simple method for measuring and computationally estimating the spatial variations of a liquid crystal phase retarder with planar alignment. The method is based on measuring the spectral transmission of the phase retarder at several spatial locations and estimating it at others. The experimental results show that the method provides an accurate spatial description of the phase retarder and can be employed for calibrating relevant systems.

Multi-aperture snapshot compressive hyperspectral camera.

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.

Compressive sensing resonator spectroscopy.

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.

Along-track scanning using a liquid crystal compressive hyperspectral imager.

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.

Compressive 4D spectro-volumetric imaging.

In this Letter, we present a method for hyperspectral imaging of three-dimensional objects. A compressive sensing approach is utilized to remedy the acquisition effort required to capture the large amount of data. The spectral dimension is compressively sensed by means of a liquid crystal-based encoder, and the volumetric data are captured using a synthetic aperture integral imaging setup. We demonstrate reconstruction of spectro-volumetric tesseracts with hundreds of spectral bands at different depths without compromise of spatial resolution.

Compressive hyperspectral microscopy for cancer detection.

Significance: Hyperspectral microscopy grants the ability to characterize unique properties of tissues based on their spectral fingerprint. The ability to label and measure multiple molecular probes simultaneously provides pathologists and oncologists with a powerful tool to enhance accurate diagnostic and prognostic decisions. As the pathological workload grows, having an objective tool that provides companion diagnostics is of immense importance. Therefore, fast whole-slide spectral imaging systems are of immense importance for automated cancer prognostics that meet current and future needs. Aim: We aim to develop a fast and accurate hyperspectral microscopy system that can be easily integrated with existing microscopes and provide flexibility for optimizing measurement time versus spectral resolution. Approach: The method employs compressive sensing (CS) and a spectrally encoded illumination device integrated into the illumination path of a standard microscope. The spectral encoding is obtained using a compact liquid crystal cell that is operated in a fast mode. It provides time-efficient measurements of the spectral information, is modular and versatile, and can also be used for other applications that require rapid acquisition of hyperspectral images. Results: We demonstrated the acquisition of breast cancer biopsies hyperspectral data of the whole camera area within ∼1 s. This means that a typical 1×1 cm2 biopsy can be measured in ∼10 min. The hyperspectral images with 250 spectral bands are reconstructed from 47 spectrally encoded images in the spectral range of 450 to 700 nm. Conclusions: CS hyperspectral microscopy was successfully demonstrated on a common lab microscope for measuring biopsies stained with the most common stains, such as hematoxylin and eosin. The high spectral resolution demonstrated here in a rather short time indicates the ability to use it further for coping with the highly demanding needs of pathological diagnostics, both for cancer diagnostics and prognostics.

Dual-camera design for hyperspectral and panchromatic imaging, using a wedge shaped liquid crystal as a spectral multiplexer.

In this paper, we present a new hyperspectral compact camera which is designed to have high spatial and spectral resolutions, to be vibrations tolerant, and to achieve state-of-the-art high optical throughput values compared to existing nanosatellite hyperspectral imaging payloads with space heritage. These properties make it perfect for airborne and spaceborne remote sensing tasks. The camera has both hyperspectral and panchromatic imaging capabilities, achieved by employing a wedge-shaped liquid crystal cell together with computational image processing. The hyperspectral images are acquired through passive along-track spatial scanning when no voltage is applied to the cell, and the panchromatic images are quickly acquired in a single snapshot at a high signal-to-noise ratio when the cell is voltage driven.

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal.

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.

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder.

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.