Adaptive filter design via a gradient thresholding algorithm for compressive spectral imaging.

Sensing a spectral image data cube has traditionally been a time-consuming task since it requires a scanning process. In contrast, compressive spectral imaging (CSI) has attracted widespread interest since it requires fewer samples than scanning systems to acquire the data cube, thus improving the sensing speed. CSI captures linear projections of the scene, and then a reconstruction algorithm estimates the underlying scene. One notable CSI architecture is the color coded aperture snapshot spectral imager (C-CASSI), which employs pixelated filter arrays as the coding patterns to spatially and spectrally encode the incoming light. Up to date works on C-CASSI have used non-adaptive color coded apertures. Non-adaptive sampling ignores prior information about the signal to design the coding patterns. Therefore, this work proposes a method to adaptively design the color coded aperture, such that the quality of image reconstruction is improved. In more detail, this work introduces a gradient thresholding algorithm, which computes the consecutive color coded aperture from a rapidly reconstructed low-resolution version of the data cube. The successive adaptive patterns enable recovering a data cube in the presence of Gaussian noise with higher image quality. Real reconstructions and simulations evidence an improvement of up to 3 dB in the quality of image reconstruction of the proposed method in comparison with state-of-the-art non-adaptive techniques.

Joint sparse and low rank recovery algorithm for compressive hyperspectral imaging.

Compressive spectral imaging techniques encode and disperse a hyperspectral image (HSI) to sense its spatial and spectral information with few bidimensional (2D) multiplexed projections. Recovering the original HSI from the 2D projections is carried by traditional compressive sensing-based techniques that exploit the sparsity property of natural HSI as they are represented in a proper orthonormal basis. Nevertheless, HSIs also exhibit a low rank property inasmuch only a few numbers of spectral signatures are present in the images. Specifically, when an HSI is rearranged as a matrix whose columns represent vectorized 2D spatial images in a different wavelength, this matrix is said to be low rank. Therefore, this paper proposes an HSI recovering algorithm from compressed measurements involving a joint sparse and low rank optimization problem, which seeks to jointly minimize the ℓ2-, ℓ1-, and ℓ*-norm, leading the solution to fit the given projections, and be simultaneously sparse and low rank. Several simulations, along different data sets and optical sensing architectures, show that when the low rank property is included in the inverse problem formulation, the reconstruction quality increases up to four (dB) in terms of peak signal to noise ratio.

3D compressive spectral integral imaging.

A novel compressive 3D imaging spectrometer based on the coded aperture snapshot spectral imager (CASSI) is proposed. By inserting a microlens array (MLA) into the CASSI system, one can capture spectral data of 3D objects in a single snapshot without requiring 3D scanning. The 3D spatio-spectral sensing phenomena is modelled by computational integral imaging in tandem with compressive coded aperture spectral imaging. A set of focal stack images is reconstructed from a single compressive measurement, and presented as images focused on different depth planes where the objects are located. The proposed optical system is demonstrated with simulations and experimental results.

Compressive spectral testbed imaging system based on thin-film color-patterned filter arrays.

Compressive spectral imaging systems can reliably capture multispectral data using far fewer measurements than traditional scanning techniques. In this paper, a thin-film patterned filter array-based compressive spectral imager is demonstrated, including its optical design and implementation. The use of a patterned filter array entails a single-step three-dimensional spatial-spectral coding on the input data cube, which provides higher flexibility on the selection of voxels being multiplexed on the sensor. The patterned filter array is designed and fabricated with micrometer pitch size thin films, referred to as pixelated filters, with three different wavelengths. The performance of the system is evaluated in terms of references measured by a commercially available spectrometer and the visual quality of the reconstructed images. Different distributions of the pixelated filters, including random and optimized structures, are explored.

DMD-based implementation of patterned optical filter arrays for compressive spectral imaging.

Compressive spectral imaging (CSI) captures multispectral imagery using fewer measurements than those required by traditional Shannon-Nyquist theory-based sensing procedures. CSI systems acquire coded and dispersed random projections of the scene rather than direct measurements of the voxels. To date, the coding procedure in CSI has been realized through the use of block-unblock coded apertures (CAs), commonly implemented as chrome-on-quartz photomasks. These apertures block or permit us to pass the entire spectrum from the scene at given spatial locations, thus modulating the spatial characteristics of the scene. This paper extends the framework of CSI by replacing the traditional block-unblock photomasks by patterned optical filter arrays, referred to as colored coded apertures (CCAs). These, in turn, allow the source to be modulated not only spatially but spectrally as well, entailing more powerful coding strategies. The proposed CCAs are synthesized through linear combinations of low-pass, high-pass, and bandpass filters, paired with binary pattern ensembles realized by a digital micromirror device. The optical forward model of the proposed CSI architecture is presented along with a proof-of-concept implementation, which achieves noticeable improvements in the quality of the reconstruction.

Multi-spectral compressive snapshot imaging using RGB image sensors.

Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color images from a single snapshot taken by a monochrome image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the image sensor pixels to account for color and then investigate the impact on image quality using either a traditional color image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon image sensor which stacks red, green, and blue pixels on top of one another.

Higher-order computational model for coded aperture spectral imaging.

Coded aperture snapshot spectral imaging systems (CASSI) sense the three-dimensional spatio-spectral information of a scene using a single two-dimensional focal plane array snapshot. The compressive CASSI measurements are often modeled as the summation of coded and shifted versions of the spectral voxels of the underlying scene. This coarse approximation of the analog CASSI sensing phenomena is then compensated by calibration preprocessing prior to signal reconstruction. This paper develops a higher-order precision model for the optical sensing in CASSI that includes a more accurate discretization of the underlying signals, leading to image reconstructions less dependent on calibration. Further, the higher-order model results in improved image quality reconstruction of the underlying scene than that achieved by the traditional model. The proposed higher precision computational model is also more suitable for reconfigurable multiframe CASSI systems where multiple coded apertures are used sequentially to capture the hyperspectral scene. Several simulations and experimental measurements demonstrate the benefits of the discretization model.