Dan Arbel

Performance limitation of laser satellite communication due to vibrations and atmospheric turbulence: down-link scenario

SUMMARY In this paper, we analyse the effects of vibrations and the atmosphere on the performance of a broadband laser inter-satellite link (BLISL) which was studied within the framework of the BLISL joint Israeli– German applied research project. The use of optical radiation as a carrier between satellites and in satelliteto-ground links enables transmission using very narrow beam divergence angles. Due to the narrow beam divergence angle and the large distance between the satellite and the ground station or airplane the pointing is a complicated process. Further complication results from vibration of the pointing system caused by two fundamental mechanisms of a stochastic nature: (1) tracking noise created by the electro-optic tracker and (2) vibrations caused by internal satellite mechanical mechanisms. Additionally an inhomogeneity in the temperature and pressure of the atmosphere leads to variations of the refractive index along the transmission path. These variations of refractive index as well as pointing vibrations can cause fluctuations in the intensity and the phase of the received signal leading to an increase in link error probability. In this paper, we develop a bit error probability (BEP) model that takes into account both pointing vibrations and turbulence-induced log amplitude fluctuations (i.e. signal intensity fading) in a regime in which the receiver aperture D0 is smaller than the turbulence coherence diameter d0: Our results indicate that BLISL can achieve a BEP of 10 � 9 and data rate of 1Gbps with normalized pointing vibration of GT *s 2 ¼ 0:05 and turbulence of sX ¼ 0:3: Copyright # 2003 John Wiley & Sons, Ltd.

Landsat TM Satellite Image Restoration Using Kalman Filters

The quality of satellite images propagating through the atmosphere is affected by phenomena such as scattering and absorption of light, and turbulence, which degrade the image by blurring it and reducing its contrast. The atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented in the digital restoration of Landsat Thematic Mapper (TM) imagery. Digital restoration results for Landsat TM imagery using the atmospheric Wiener filter were presented in the past. Here, a new approach for digital restoration of Landsat TM imagery is presented by implementing a Kalman filter as an atmospheric filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously. Turbulence MTF is calculated from meteorological data. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in both the atmospheric Wiener and Kalman filters. Restoration improves both resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Although aerosol MTF is dominant, slightly better results are obtained when the shape of atmospheric MTF includes turbulence, in addition to that of aerosol MTF, as shown by the use of criteria for restoration success. In general, the Kalman restoration is superior.

Imaging through the atmosphere: an overview

Atmospheric blur is usually attributed in the remote sensing community to forward scatter of light by aerosols, called the adjacency effect, and in the propagation community to optical turbulence. It is our view that both phenomena contribute to atmospheric blur. In some situations such as lines-of-sites close to the ground turbulence is significant, while in others, such as lines of sight with optical depths on the order of unit or more, aerosol blur is significant. However, in general both types of blur should be considered. Examples are cited in which ignoring aerosol scatter leads to incorrect conclusions or in which ignoring turbulence leads to only partial image correction. Both vertical nd horizontal imagin are considered. The purpose of the paper is to emphasize the need for both the remote sensing and propagation communities to consider both aerosol blur and turbulence blue in analyses of experimental results.

Medical Image Restoration of Dynamic Lungs using Optical Transfer Function of Lung Motion

When carrying out medical imaging based on detection of isotopic radiation levels of internal organs such a lungs or heart, distortions, and blur arise as a result of the organ motion during breathing and blood supply. Consequently, image quality declines, despite the use of expensive high resolution devices and, such devices are not exploited fully. A method with which to overcome the problem is image restoration. Previously, we suggested and developed a method for calculating numerically the optical transfer function (OTF) for any type of image motion. The purpose of this research is restoration of original isotope images (of the lungs) by restoration methods that depend on the OTF of the real time relative motion between the object and the imaging system. This research uses different algorithms for the restoration of an image, according to the OTF of the lung motion, which is in several directions simultaneously. One way of handling the three-dimensional movement is to decompose the image into several portions, to restore each portion according to its motion characteristics, and then to combine all the image portions back into a single image. An additional complication is that the image was recorded at different angles. The application of this research is in medical systems requiring high resolution imaging. The main advantage of this approach is its low cost versus conventional approaches.

Criteria for satellite image restoration success

Many properties of the atmosphere affect the quality of images propagating through it by blurring and reducing their contrast. The atmospheric path involves several limitations, such as scattering, absorption of light, and turbulence, which degrade the image. Recently developed atmospheric filters, which correct for turbulence blur, aerosol blur, and path radiance simultaneously, are implemented here in the digital restoration of Landsat thematic mapper (TM) imagery. The turbulence modulation transfer function (MTF) is calculated from meteorological data or estimated if no meteorological data were measured. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in the atmospheric filter. Restoration improves smallness of size of both resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. A way to evaluate restoration improvement is presented here by the use of quantitative criteria as well as subjective opinions of human observers in perception experiments. Not all the restoration criteria represent improvement in the same tested image under the same restoration conditions. When one criterion suggests an enhancement, there is a chance that another one might represent a lower value for restoration success.

Satellite image restoration filter comparison

The quality of satellite images propagating through the atmosphere is affected by phenomena such as scattering and absorption of the light, and turbulence, which degrade the image by blurring it and reducing its contrast. Here, a new approach for digital restoration of Landsat thematic mapper (TM) imagery is presented by implementing several filters as atmospheric filters which correct for turbulence blur, aerosol blur, and path radiance simultaneously. Aerosol modulation transfer function (MTF) is consistent with optical depth. Turbulence MTF is calculated from meteorological data. The product of the two yields atmospheric MTF, which is implemented in the atmospheric filters. Restoration improves both smallness of size of resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Different restoration results are obtained by trying to restore the degraded image. Here, restorations results of the Kalman filter and the atmospheric Wiener filter are presented along with restoration results based on wavelets and multifractals. A way to determine which is the best restoration result and how good is the restored image is presented by a visual comparison and by examining several mathematical criteria.

Landsat TM satellite image restoration using Kalman filter

Satellites orbit the Earth and obtain continuous imagery of the ground below along their orbital path. The quality of satellite images propagating through the atmosphere is affected by phenomena such as scattering and absorption of light, and turbulence, which degrade the image by blurring it and reducing its contrast. The atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented in digital restoration of Landsat TM (Thematic Mapper) imagery. Digital restoration results of Landsat TM imagery using the atmospheric Wiener filter were presented in the past. Here, a new approach for digital restoration of Landsat TM is presented by implementing a Kalman filter as an atmospheric filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously. Turbulence MTF is calculated from meteorological data or estimated if no meteorological data were measured. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in both the atmospheric Wiener and Kalman filters. Restoration improves both smallness of size of resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Here, restorations results of the atmospheric Kalman filter are presented along with those for the atmospheric Wiener filter. A way to determine which is the best restoration result and how good is the restored image is presented by a visual comparison and by considering several mathematical criteria. In general the Kalman restoration is superior, and inclusion of turbulence blur also leads to slightly improved restoration.

Imaging vertically through the atmosphere: restoration of satellite images based on atmospheric MTF evaluation

When carrying out satellite images by imaging vertically through the atmosphere, distortions and blur arise as a result of turbulence and aerosols. Contrast is reduced by path radiance. The recently developed atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented in digital restoration of Landsat imagery over seven wavelength bands of the satellite instrumentation. A required input is weather. Restoration is most impressive for high optical depth situations, which cause larger blue. Restoration improves both smallness of size of resolvable detail and contrast. Turbulence modulation transfer function (MTF) is calculated from meteorological data. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in the atmospheric Wiener filter. Turbulence blue, aerosol blur, and path radiance contrast loss are all corrected simultaneously, as if there were in intervening atmosphere. The primary source of atmospheric blur is seen to be aerosol forward scatter of light. Restorations are shown for various wavelength bands and are quite apparent even under clear weather conditions.

Image reconstruction from power spectral data

Restoration of images blurred by an optical transfer function (OTF), or additive Gaussian noise which affect the Fourier transform amplitude and phase of the image, are considered. A method for reconstructing a two-dimensional image from power spectral data is presented. It is known that the spatial frequencies at which the Fourier transform F(u,v) of an image equals zero are called the real-plane zeros. It has been shown that real-plane zero locations have a significant effect on the Fourier phase in that they are the end points of phase function branch cuts, and it has been shown that real-plane zero locations can be estimated from Fourier transform magnitude data. Thus, real-plane zeros can be utilized in phase retrieval algorithms to help constrain the possible Fourier transform phase function. The purpose of this research is to recover the Fourier transform phase function from the knowledge of the power spectrum itself. By locating the points at which the Fourier transform intensity data are zero, we approximate a nonfactorizable function by its point-zero factors to recover an estimate of the object. A simple iterative method then successfully refines this phase estimate. The basic idea for the restoration is to separate the point-zeros of the modulation transfer function (MTF) or the additive noise from the point-zeros of the original image. Image restoration results according to the method of phase function retrieval for images degraded by additive noise and linear MTF are also presented.

Medical image restoration of dynamic lungs using optical transfer function of lung motion

When carrying out medical imaging based on detection of isotopic radiation levels of internal organs such as lungs or heart, distortions and blur arise as a result of the organ motion during breathing and blood supply. Consequently, the image quality declines, despite the use of expensive high resolution devices. Hence, such devices are not exploited fully. There is a need to overcome the problem in alternative ways. Such as alternative is image restoration. We suggested and developed a method for calculating numerically the optical transfer function (OTF) for any type of image motion. The purpose of this reserach is restoration of original isotope images (of the lungs) by reconstruction methods that depend on the OTF of the real time relative motion between the object and the imaging system. This research uses different algorithms for the reconstruction of an image, according to the OTF of the lung motion, which is in several directions simultaneously. One way of handling the 3D movement is to decompose the image into several portions, to restore each portion according to its motion characteristics, and then to combine all the image portions back into a single image. As additional complication is that the image was recorded at different angles. The application of this reserach is in medical systems requiring high resolution imaging. The main advantage of this approach is its low cost versus conventional approaches.