High-resolution image reconstruction from sparse data is an important problem in sensor array imaging (SAI). The reconstructed images from such a data are poorly resolved. However, there may exist possibilities of making multiple measurements, for example, collecting many frames of data in a dynamic scene situation where there is relative motion between the object and the receiver. We discuss here a method of reconstructing good-quality images from multiple frames of sparse data obtained from a simulated dynamic scene situation. This method, based on projection onto convex sets (POCS), not only restricts the solution set by satisfying the constraints in the multiple measurements but also reconstructs a high-resolution image. © 1993 Kluwer Academic Publishers.

R. Ramaseshan

Sensors

HIGH-RESOLUTION IMAGE RECONSTRUCTION

MULTIPLE LINE SPARSE DATA

PROJECTION ONTO CONVEX SETS (POCS)

SENSOR ARRAY IMAGING (SAI)

SIMULATED DYNAMIC SCENE SITUATION

Image reconstruction

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

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IIT Madras

Image reconstruction from multiple frames of sparse data

Multidimensional Systems and Signal Processing

Ill-Posed Problems in Natural Sciences

Modified Tikhonov regularization for nonlinear ill-posed problems leading to optimal convergence rates

Acoustical Imaging

Improving Resolution of Sensor Array Images Using Multiple Frames of Data

IEEE Transactions on Acoustics, Speech, and Signal Processing

Recursive reconstruction of high resolution image from noisy undersampled multiframes

Proceedings of the IEEE

Iterative methods for image deblurring

In this paper, the problem of signal recovery from partial information with special reference to sensor array imaging situations is examined. In sensor array imaging, as used for viewing underwater objects, the complex wave field is measured at a discrete set of points on a receiver airier. This data is used to reconstruct an image of the object using a suitable transformation. This data can be viewed as partial magnitude and phase data, which after appropriate phase modification, is transformed to obtain the object information. Recently, algorithms have been proposed in the literature for reconstruction of signals from partial information such as magnitude or phase (1-bit phase in some cases) of the Fourier transform. We examine the relevance of these algorithms to sensor array imaging situations. We propose new algorithms that are applicable to these situations. In particular, we show that measurement at multiple frequencies with suitable phase quantization reduces the receiver complexity and sometimes the effects of noise. The proposed new algorithms are demonstrated with several illustrations of simulated field data for a simplified model of an imaging setup. © 1990.

Signal Processing

Signal reconstruction from partial data for sensor array imaging applications

Journal	Multidimensional Systems and Signal Processing
Publisher	Kluwer Academic Publishers
ISSN	09236082
Open Access	No