Engineering topics

Pina, R. K.

Publications and source records attributed to Pina, R. K..

6 records

NASA NTRS1994

Beyond maximum entropy: Fractal Pixon-based image reconstruction

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.

Puetter, Richard C.↗

NASA NTRS1994

10 and 20 micron imaging with arrays

We discuss imaging with arrays in the thermal IR. Aspects of the design and performance of the 'Golden Gopher', an infrared array camera are presented. This instrument operates in a high-background environment, for ground-based astronomical imaging from 5 to 27 micrometers. It is built around a 20 x 64 element Si:As Impurity Band Conduction (IBC) device manufactured by GenCorp Aerojet Electronic Systems Division, and has a noise-equivalent flux density (NEFD) of 23.5 mJy (min(exp -1/2) (arcsec(exp -2) at lambda = 10 micrometers, delta-lambda = 1 micrometer, on the Mt. Lemmon 1.5m telescope. We present and discuss a sample of the data. In addition we discuss the design and expected performance of the 'Long Wavelength Spectrometer' which is now under construction for the Keck telescope.

Jones, B.↗

NASA NTRS1994

Beyond maximum entropy: Fractal pixon-based image reconstruction

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other methods, including Goodness-of-Fit (e.g. Least-Squares and Lucy-Richardson) and Maximum Entropy (ME). Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME.

Puetter, R. C.↗

NASA NTRS1993

Bayesian image reconstruction - The pixon and optimal image modeling

In this paper we describe the optimal image model, maximum residual likelihood method (OptMRL) for image reconstruction. OptMRL is a Bayesian image reconstruction technique for removing point-spread function blurring. OptMRL uses both a goodness-of-fit criterion (GOF) and an 'image prior', i.e., a function which quantifies the a priori probability of the image. Unlike standard maximum entropy methods, which typically reconstruct the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the image. We show how an optimal basis for image representation can be selected and in doing so, develop the concept of the 'pixon' which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness-of-fit criterion, OptMRL uses the maximum residual likelihood probability distribution introduced previously by Pina and Puetter (1992). This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.

Pina, R. K.↗

NASA NTRS1992

12 and 20 micron imaging of the starburst galaxy NGC 253

The study presents 12- and 20-micron imaging of the starburst galaxy NGC 253 with a spatial resolution of 0.8 arcsec. A positional uncertainty of 1.6 arcsec in these images is estimated on the basis of previous ground-based mid-IR studies to determine the absolute position of the images. It is concluded that the proposed 'nucleus' of NGC 253, i.e., the dominant, bright, flat-spectrum radio source identified by Turner & Ho (1985) (TH 2), is not associated with the mid-IR peak, but rather is located 2.2 arcsec to the northeast. The mid-IR peak, IRS 1, is placed midway between TH 6 and TH 7. Several coincidences with the present 12-micron image are found: IRS 1 falls within 1 arcsec of the 2-micron peak; a second significantly weaker IR source, IRS 2, coincides with TH 2; and the brightest steep-spectrum 6-cm radio source, TH 9, coincides with a 'tongue' of emission extending to the southwest of IRS 1.

Pina, R. K.↗

NASA NTRS1992

Incorporation of spatial information in Bayesian image reconstruction - The maximum residual likelihood criterion

We have developed a new figure of merit, a 'maximum-residual-likelihood' (MRL) statistic, for the goodness of fit for Bayesian image restoration which explicitly incorporates spatial information. The MRL constraint provides a natural means of incorporating the prior knowledge that the residuals contain no spatial structure through the autocorrelation function of the residuals. We demonstrate that this statistic follows a Chi-square distribution and that forcing this statistic to have its most probable value leads to a restored image whose residuals are consistent with the noise model. Our numerical experiments suggest that image restoration using the MRL statistic alone is numerically robust and produces results which are independent of the initial guess for the restored image. However, we caution that using the MRL statistic without an image prior can result in overresolution in low SNR portions of the image.

Pina, R. K.↗