Andrzej K. Brodzik

High-throughput hyperspectral infrared camera

A spectral imager constructs a three-dimensional (two spatial and one spectral) image from a series of two-dimensional images. We discuss a technique for spectral imaging that multiplexes the spatial and spectral information on a staring focal plane and then demultiplexes the resulting imagery to obtain the spectral image. The spectral image consists of 100×100 spatial pixels and 25 spectral bands. The current implementation operates over the 3–5-μm band, but can easily be applied to other spectral regions. This approach to spectral imaging has high optical throughput and is robust to focal plane array nonuniformities. A hardware description, the mathematical development, and experimental results are presented.

Compact visible/near-infrared hyperspectral imager

This paper reports on the design, performance and signal processing of a visible/near infrared (VIS-NIR) chromotomographic hyperspectral imaging sensor. The sensor consists of a telescope, a direct vision prism, and a framing video camera. The direct vision prism is a two-prism set, arranged such that one wavelength passes undeviated, while the other wavelengths are dispersed along a line. The prism is mounted on a bearing so that it can be rotated on the optical axis of the telescope. As the prism is rotated, the projected image is multiplexed on elements of the focal plane array. Computational methods are used to reconstruct the scene at each wavelength; an approach similar to the limited-angle tomography techniques used in medicine. The sensor covers the visible through near infrared spectrum of silicon photodiodes. The sensor weighs less than 6 pounds has under 300 in3 volume and requires 20 watts. It produces image cubes, with 64 spectral bands, at rates up to 10 Hz. By operating in relatively fast framing mode, the sensor allows characterization of transient events. We will describe the sensor configuration and method of operation. We also present examples of sensor spectral image data.

Convex projections algorithm for restoration of limited-angle chromotomographic images

We present a new algorithm for image restoration in limited-angle chromotomography. The algorithm is a generalization of the technique considered previously by the authors, based on a hybrid of a direct method of inversion and the iterative method of projections onto convex sets. The generalization is achieved by introducing a new object domain constraint. This constraint takes advantage of hyperspectral data redundancy and is realized by truncating the singular-value decomposition of the spatial–chromatic image matrix. As previously, the transform domain constraint is defined in terms of nonzero singular values of the system transfer function matrix. The new algorithm delivers high image fidelity, converges rapidly, and is easy to implement. Results of experiments on real data are included.

Data restoration in chromotomographic hyperspectral imaging

Recently, a new approach to hyperspectral imaging, relying on the theory of computed tomography, was proposed by researchers at the Air Force Research Laboratory. The approach allows all photons to be recorded and therefore increases robustness of the imaging system to noise and focal plane array non-uniformities. However, as all computed tomography systems, the approach suffers form the limited angle problem, which obstructs reconstruction of the hyperspectral information. In this work we present a direct, one-step algorithm for reconstruction of the unknown information based on a priori knowledge about the hyperspectral image.

Principal component analysis in limited-angle chromotomography

Chromotomographic spectral imaging techniques offer high spatial resolution, moderate spectral resolution and high optical throughput. However, the performance of chromotomographic systems has historically been limited by the artifacts introduced by a cone of missing information. The recent successful application of principal component analysis to spectral imagery indicates that spectral imagery is inherently redundant. We have developed an iterative technique for filling in the missing cone that relies on this redundance. We demonstrate the effectiveness of our approach on measured data, and compare the results to those obtained with a scanned slit configuration.

Image restoration by convex projections: applications to image spectrometry

We present a new algorithm for image restoration with application to image spectrometry, combining two radically different techniques: the singular value decomposition (SVD) and the method of projections onto convex sets (POCS). The SVD technique is used to obtain an initial estimate of the unknown image and to establish correspondence between the missing data and the spectral description of the image. The iterative method of convex projections is then applied to the estimate, regaining the missing data by enforcing a sequence of constraints on the reconstructed object. We report results of investigations of the SVD-POCS method and demonstrate that the new algorithm leads to significant improvements in the recovered image.

New iterative algorithm for restoration of chromotomographic images

We present a new algorithm for chromotomographic image restoration. The main stage of the algorithm employs the iterative method of projections onto convex sets, utilizing a new constraint operator. The constraint takes advantage of hyperspectral data redundancy and information compacting ability of singular value decomposition to reduce noise and artifacts. Results of experiments on both in-house and AVIRIS data demonstrate that the algorithm converges rapidly and delivers high image fidelity.