Traditional spectral imaging approaches require sensing all the voxels of a scene. Colored mosaic FPA detector-based architectures can acquire sets of the scene’s spectral components, but the number of spectral planes depends directly on the number of available filters used on the FPA, which leads to reduced spatiospectral resolutions. Instead of sensing all the voxels of the scene, compressive spectral imaging (CSI) captures coded and dispersed projections of the spatiospectral source. This approach mitigates the resolution issues by exploiting optical phenomena in lenses and other elements, which, in turn, compromise the portability of the devices. This paper presents a compact snapshot colored compressive spectral imager (SCCSI) that exploits the benefits of the colored mosaic FPA detectors and the compression capabilities of CSI sensing techniques. The proposed optical architecture has no moving parts and can capture the spatiospectral information of a scene in a single snapshot by using a dispersive element and a color-patterned detector. The optical and the mathematical models of SCCSI are presented along with a testbed implementation of the system. Simulations and real experiments show the accuracy of SCCSI and compare the reconstructions with those of similar CSI optical architectures, such as the CASSI and SSCSI systems, resulting in improvements of up to 6 dB and 1 dB of PSNR, respectively.

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.

Coded aperture snapshot spectral imaging (CASSI) provides a mechanism for capturing a 3D spectral cube with a single shot 2D measurement. In many applications selective spectral imaging is sought since relevant information often lies within a subset of spectral bands. Capturing and reconstructing all the spectral bands in the observed image cube, to then throw away a large portion of this data, is inefficient. To this end, this paper extends the concept of CASSI to a system admitting multiple shot measurements, which leads not only to higher quality of reconstruction but also to spectrally selective imaging when the sequence of code aperture patterns is optimized. The aperture code optimization problem is shown to be analogous to the optimization of a constrained multichannel filter bank. The optimal code apertures allow the decomposition of the CASSI measurement into several subsets, each having information from only a few selected spectral bands. The rich theory of compressive sensing is used to effectively reconstruct the spectral bands of interest from the measurements. A number of simulations are developed to illustrate the spectral imaging characteristics attained by optimal aperture codes.

Multi-shot coded aperture snapshot spectral imaging (CASSI) systems capture the spectral information of a scene using a small set of coded focal plane array (FPA) compressive measurements. Compressed sensing (CS) reconstruction algorithms are then used to reconstruct the underlying spectral 3D data cube from an underdetermined system of linear equations. Multiple snapshots result in a less ill-posed inverse problem and improved reconstructions. The only varying components in CASSI are the coded apertures, whose structure is crucial inasmuch as they determine the minimum number of FPA measurements needed for correct image reconstruction and the corresponding attainable quality. Traditionally, the spatial structures of the coded aperture entries are selected at random, leading to suboptimal reconstruction solutions. This work presents an optimal structure design of a set of coded apertures by optimizing the concentration of measure of the multi-shot CASSI sensing matrix and its incoherence with respect to the sparse representation basis. First, the CASSI matrix system representation in terms of the ensemble of random projections is established. Then, the restricted isometry property (RIP) of the CASSI projections is determined as a function of the coded aperture entries. The optimal coded aperture structures are designed under the criterion of satisfying the RIP with high probability, coined spatiotemporal blue noise (BN) coded apertures. Furthermore, an algorithm that implements the BN ensembles is presented. Extensive simulations and a testbed implementation are developed to illustrate the improvements of the BN coded apertures over the traditionally used coded aperture structures, in terms of spectral image reconstruction PSNR and SSIM.

Optical proximity correction (OPC) and phase-shifting mask (PSM) are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. Traditional RETs, however, fix the source thus limiting the degrees of freedom during the optimization of the mask patterns. To overcome this restriction, a set of simultaneous source and mask optimization (SMO) methods have been developed recently where the resulting source and mask patterns fall well outside the realm of known design forms. This paper focuses on developing computationally efficient, pixel-based, simultaneous source mask optimization methods for both OPC and PSM designs, where the synergy is exploited in the joint optimization of source and mask patterns. The Fourier series expansion model is applied to approximate the partially coherent system as a sum of coherent systems. Cost sensitivity is used to drive the output pattern error in the descent direction. In order to influence the solution patterns to have more desirable manufacturability properties, topological constraints are added to the optimization framework. Several illustrative simulations are presented to demonstrate the effectiveness of the proposed algorithms.