Oversampling requirements for pixelated-imager systems

Abstract. The image quality resulting from a 2-D image-sampling process by an array of pixels is described. The description is based on a Fourier transformation of the Wigner-Seitz cell, which transforms a unit cell of the sampling lattice in the spatial domain into a bandwidth cell in the spatial-frequency domain. The area of the resulting bandwidth cell is a quantitative measure of the image fidelity of the sampling process. We compare the image-quality benefits of three different oversampling geometries in terms of the modulation transfer function (MTF) as a function of the amount of oversampling used. © 1999 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(99

ARGOS testbed: study of multidisciplinary challenges of future spaceborne interferometric arrays

Abstract. Future spaceborne interferometric arrays must meet stringent optical performance and tolerance requirements while exhibiting modularity and acceptable manufacture and integration cost levels. The Massachusetts Institute of Technology (MIT) Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) is a wide-angle Fizeau interferometer spacecraft testbed designed to address these research challenges. Designing a space-based stellar interferometer, which requires tight tolerances on pointing and alignment for its apertures, presents unique multidisciplinary challenges in the areas of structural dynamics, controls, and multiaperture phasing active optics. In meeting these challenges, emphasis is placed on modularity in spacecraft subsystems and optics as a means of enabling expandability and upgradeability. A rigorous theory of beam-combining errors for sparse optical arrays is derived and flown down to the design of various subsystems. A detailed elaboration on the optics system and control system is presented based on the performance requirements and beam-combining error tolerances. The space environment is simulated by floating ARGOS on a frictionless airbearing that enables it to track both fast and slow moving targets

A New Image Filtering Technique Combining a Wavelet Transform with a Linear Neural Network: Application to Face Recognition

(2000, Optical Engineering, 38, 2894–2899) In order to improve the performance of a linear auto-associator (which is a neural network model), we explore the use of several pre-processing techniques. The gist of our approach is to represent each pattern by one or several pre-processed (i.e. filtered) versions of the original pattern (plus the original pattern). First, we compare the performance of several pre-processing techniques (a plain vanilla version of the auto-associator as a control, a Sobel operator, a Canny-Deriche operator, and a multiscale Canny-Deriche operator) and a Wiener filter on a pattern completion task using a noise degraded version of faces stored. We found that the multiscale Canny-Deriche operator gives the best performance of all models. Second, we compare the performance of the multiscale Canny-Deriche operator with the control condition on a pattern completion task of noise degraded versions (with several levels of noise) of learned faces and new faces of the same or another race than the learned faces. In all cases, the multiscale Canny-Deriche operator performs significantly better than the control. keywords: auto-associator, multiscale Canny-Deriche operator, pattern recognition, wavelet transform, Wiener filter, Principal Component Analysis.