Spatially variant apodization for image reconstruction from partial Fourier data.

Sidelobe artifacts are a common problem in image reconstruction from finite-extent Fourier data. Conventional shift-invariant windows reduce sidelobe artifacts only at the expense of worsened mainlobe resolution. Spatially variant apodization (SVA) was previously introduced as a means of reducing sidelobe artifacts, while preserving mainlobe resolution. Although the algorithm has been shown to be effective in synthetic aperture radar (SAR), it is heuristically motivated and it has received somewhat limited analysis. In this paper, we show that SVA is a version of minimum-variance spectral estimation (MVSE). We then present a complete development of the four types of two-dimensional SVA for image reconstruction from partial Fourier data. We provide simulation results for various real-valued and complex-valued targets and point out some of the limitations of SVA. Performance measures are presented to help further evaluate the effectiveness of SVA.

High-resolution planetary imaging via spotlight-mode synthetic aperture radar.

We consider the application of a spotlight-mode synthetic aperture radar (SAR) imaging technique to the problem of high-resolution lunar imaging and other related radar astronomy problems. This approach offers improved image quality, compared with conventional processing, at the expense of slightly increased computational effort. Results of the processing of lunar data acquired with the 12.6 cm wavelength radar system at Arecibo Observatory are presented, and compared with the best available published result, by Stacy (1993), which uses focusing techniques from stripmap SAR.

Partial Radon transforms.

This article formally defines partial Radon transforms for functions of more than two dimensions. It shows that a generalized projection-slice theorem exists which connects planar and hyperplanar projections of a function to its Fourier transform. In addition, a general theoretical framework is provided for carrying out n-dimensional backprojection reconstruction in a multistage fashion through the use of the partial Radon transform.

A Hankel transform approach to tomographic image reconstruction.

A relatively unexplored algorithm is developed for reconstructing a two-dimensional image from a finite set of its sampled projections. The algorithm, referred to as the Hankel-transform-reconstruction (HTR) algorithm, is polar-coordinate based. The algorithm expands the polar-form Fourier transform F(r,theta) of an image into a Fourier series in theta calculates the appropriately ordered Hankel transform of the coefficients of this series, giving the coefficients for the Fourier series of the polar-form image f(p,phi); resolves this series, giving a polar-form reconstruction; and interpolates this reconstruction to a rectilinear grid. The HTR algorithm is outlined, and it is shown that its performance compares favorably to the popular convolution-backprojection algorithm.