Figen S. Oktem

Exact Relation Between Continuous and Discrete Linear Canonical Transforms

Linear canonical transforms (LCTs) are a family of integral transforms with wide application in optical, acoustical, electromagnetic, and other wave propagation problems. The Fourier and fractional Fourier transforms are special cases of LCTs. We present the exact relation between continuous and discrete LCTs (which generalizes the corresponding relation for Fourier transforms), and also express it in terms of a new definition of the discrete LCT (DLCT), which is independent of the sampling interval. This provides the foundation for approximately computing the samples of the LCT of a continuous signal with the DLCT. The DLCT in this letter is analogous to the DFT and approximates the continuous LCT in the same sense that the DFT approximates the continuous Fourier transform. We also define the bicanonical width product which is a generalization of the time-bandwidth product.

Equivalence of linear canonical transform domains to fractional Fourier domains and the bicanonical width product: a generalization of the space-bandwidth product

Linear canonical transforms (LCTs) form a three-parameter family of integral transforms with wide application in optics. We show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and ordered by the corresponding fractional order parameter and provides insight into the evolution of light through an optical system modeled by LCTs. If a set of signals is highly confined to finite intervals in two arbitrary LCT domains, the space-frequency (phase space) support is a parallelogram. The number of degrees of freedom of this set of signals is given by the area of this parallelogram, which is equal to the bicanonical width product but usually smaller than the conventional space-bandwidth product. The bicanonical width product, which is a generalization of the space-bandwidth product, can provide a tighter measure of the actual number of degrees of freedom, and allows us to represent and process signals with fewer samples.

Phase-space window and degrees of freedom of optical systems with multiple apertures

We show how to explicitly determine the space-frequency window (phase-space window) for optical systems consisting of an arbitrary sequence of lenses and apertures separated by arbitrary lengths of free space. If the space-frequency support of a signal lies completely within this window, the signal passes without information loss. When it does not, the parts that lie within the window pass and the parts that lie outside of the window are blocked, a result that is valid to a good degree of approximation for many systems of practical interest. Also, the maximum number of degrees of freedom that can pass through the system is given by the area of its space-frequency window. These intuitive results provide insight and guidance into the behavior and design of systems involving multiple apertures and can help minimize information loss.

Image formation model for photon sieves

A photon sieve, modification of a Fresnel zone plate, has been recently proposed to achieve higher resolution imaging and spectroscopy at UV and x-ray wavelengths. In this paper, we present Fresnel imaging formulas that relate the output of a photon sieve imaging system to its input, originating from either a coherent or incoherent extended source. By using a well-known model for the zone plate, we also provide approximations to these imaging formulas, which are more efficient to compute. These imaging relations for both photon sieve and the approximate model are crucial for effectively analyzing and solving the inverse problems that arise from the new imaging modalities enabled by photon sieves. We illustrate this by formulating the forward model of the image formation process for an application in polychromatic imaging.

Linear Canonical Domains and Degrees of Freedom of Signals and Systems

We discuss the relationships between linear canonical transform (LCT) domains, fractional Fourier transform (FRT) domains, and the space-frequency plane. In particular, we show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and monotonically ordered by the corresponding fractional order parameter and provides a more transparent view of the evolution of light through an optical system modeled by LCTs. We then study the number of degrees of freedom of optical systems and signals based on these concepts. We first discuss the bicanonical width product (BWP), which is the number of degrees of freedom of LCT-limited signals. The BWP generalizes the space-bandwidth product and often provides a tighter measure of the actual number of degrees of freedom of signals. We illustrate the usefulness of the notion of BWP in two applications: efficient signal representation and efficient system simulation. In the first application we provide a sub-Nyquist sampling approach to represent and reconstruct signals with arbitrary space-frequency support. In the second application we provide a fast discrete LCT (DLCT) computation method which can accurately compute a (continuous) LCT with the minimum number of samples given by the BWP. Finally, we focus on the degrees of freedom of first-order optical systems with multiple apertures. We show how to explicitly quantify the degrees of freedom of such systems, state conditions for lossless transfer through the system and analyze the effects of lossy transfer.

Fast Algorithms for Digital Computation of Linear Canonical Transforms

Fast and accurate algorithms for digital computation of linear canonical transforms (LCTs) are discussed. Direct numerical integration takes O(N2) time, where N is the number of samples. Designing fast and accurate algorithms that take \(O(N\log N)\) time is of importance for practical utilization of LCTs. There are several approaches to designing fast algorithms. One approach is to decompose an arbitrary LCT into blocks, all of which have fast implementations, thus obtaining an overall fast algorithm. Another approach is to define a discrete LCT (DLCT), based on which a fast LCT (FLCT) is derived to efficiently compute LCTs. This strategy is similar to that employed for the Fourier transform, where one defines the discrete Fourier transform (DFT), which is then computed with the fast Fourier transform (FFT). A third, hybrid approach involves a DLCT but employs a decomposition-based method to compute it. Algorithms for two-dimensional and complex parametered LCTs are also discussed.

High-resolution computational spectral imaging with photon sieves

Photon sieves, modifications of Fresnel zone plates, are a new class of diffractive image forming devices that open up new possibilities for high resolution imaging and spectroscopy, especially at UV and x-ray regime. In this paper, we develop a novel computational photon sieve imaging modality that enables high-resolution spectral imaging. For the spatially incoherent illumination, we study the problem of recovering the individual spectral images from the superimposed and blurred measurements of the proposed photon sieve system. This inverse problem, which can be viewed as a multi frame deconvolution problem involving multiple objects, is formulated as a maximum posterior estimation problem, and solved using a fixed-point algorithm. The performance of the proposed technique is illustrated for EUV spectral imaging through numerical simulations. The results suggest that higher spatial and spectral resolution can be achieved as compared to conventional spectral imagers.

A Parametric Estimation Approach to Instantaneous Spectral Imaging

Spectral imaging, the simultaneous imaging and spectroscopy of a radiating scene, is a fundamental diagnostic technique in the physical sciences with widespread application. Due to the intrinsic limitation of two-dimensional (2D) detectors in capturing inherently three-dimensional (3D) data, spectral imaging techniques conventionally rely on a spatial or spectral scanning process, which renders them unsuitable for dynamic scenes. In this paper, we present a nonscanning (instantaneous) spectral imaging technique that estimates the physical parameters of interest by combining measurements with a parametric model and solving the resultant inverse problem computationally. The associated inverse problem, which can be viewed as a multiframe semiblind deblurring problem (with shift-variant blur), is formulated as a maximum a posteriori (MAP) estimation problem since in many such experiments prior statistical knowledge of the physical parameters can be well estimated. Subsequently, an efficient dynamic programming algorithm is developed to find the global optimum of the nonconvex MAP problem. Finally, the algorithm and the effectiveness of the spectral imaging technique are illustrated for an application in solar spectral imaging. Numerical simulation results indicate that the physical parameters can be estimated with the same order of accuracy as state-of-the-art slit spectroscopy but with the added benefit of an instantaneous, 2D field-of-view. This technique will be particularly useful for studying the spectra of dynamic scenes encountered in space remote sensing.

Cramer-Rao bounds and instrument optimization for slitless spectroscopy

Spectroscopy is a fundamental diagnostic technique in physical sciences with widespread application. Multi-order slitless imaging spectroscopy has been recently proposed to overcome the limitations of traditional spectrographs, in particular their small instantaneous field of view. Since an inversion is required to infer the physical parameters of interest from slitless spectroscopic measurements, a rigorous theory is essential for quantitative characterization of their performance. In this paper we develop such a theory using the Cramer-Rao lower bounds for the physical parameters of interest, which are derived in terms of important instrument design considerations including the spectral orders to measure, dispersion scale, signal-to-noise ratio, and number of pixels. Our treatment provides a framework for exploring the optimal choices of these design considerations. We illustrate these concepts for an application in EUV solar spectroscopy.

Analytical precision limits in slitless spectroscopy

We consider the problem of estimating emission line parameters from the measurements of a multi-order slitless spectrometer. This problem can be viewed as a multi-frame deblurring problem with shift variant Gaussian blur. By using Cramer-Rao lower bound theory, we derive analytical precision limits to this parameter estimation when the measurements are corrupted with Gaussian noise. The derivation involves an approximation to the Fisher information matrix in order to obtain closed-form expressions. An important feature of our treatment is to provide a framework for exploring the optimized instrument requirements, including various potential observing scenarios, spatial and spectral resolutions, and signal-to-noise ratio.