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Results for “Spatially Varying Blur”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1974

An investigation of the facsimile camera response to object motion

A general analytical model of the facsimile camera response to object motion is derived as an initial step toward characterizing the resulting image degradation. This model expresses the spatial convolution of a time-varying object radiance distribution and camera point-spread function for each picture element in the image. Time variations and these two functions during each convolution account for blurring of small image detail, and variations between, as well as during, successive convolutions account for geometric image distortions. If the object moves beyond the angular extent of several picture elements while it is being imaged, then geometric distortion tends to dominate blurring as the primary cause of image degradation. The extent of distortion depends not only on object size and velocity but also on the direction of object motion, and is therefore difficult to classify in a general sense.

Huck, F. O.↗
NASA NTRS1990

Hybrid vision activities at NASA Johnson Space Center

NASA's Johnson Space Center in Houston, Texas, is active in several aspects of hybrid image processing. (The term hybrid image processing refers to a system that combines digital and photonic processing). The major thrusts are autonomous space operations such as planetary landing, servicing, and rendezvous and docking. By processing images in non-Cartesian geometries to achieve shift invariance to canonical distortions, researchers use certain aspects of the human visual system for machine vision. That technology flow is bidirectional; researchers are investigating the possible utility of video-rate coordinate transformations for human low-vision patients. Man-in-the-loop teleoperations are also supported by the use of video-rate image-coordinate transformations, as researchers plan to use bandwidth compression tailored to the varying spatial acuity of the human operator. Technological elements being developed in the program include upgraded spatial light modulators, real-time coordinate transformations in video imagery, synthetic filters that robustly allow estimation of object pose parameters, convolutionally blurred filters that have continuously selectable invariance to such image changes as magnification and rotation, and optimization of optical correlation done with spatial light modulators that have limited range and couple both phase and amplitude in their response.

Juday, Richard D.↗
NASA NTRS1983

Information theory analysis of sensor-array imaging systems for computer vision

Information theory is used to assess the performance of sensor-array imaging systems, with emphasis on the performance obtained with image-plane signal processing. By electronically controlling the spatial response of the imaging system, as suggested by the mechanism of human vision, it is possible to trade-off edge enhancement for sensitivity, increase dynamic range, and reduce data transmission. Computational results show that: signal information density varies little with large variations in the statistical properties of random radiance fields; most information (generally about 85 to 95 percent) is contained in the signal intensity transitions rather than levels; and performance is optimized when the OTF of the imaging system is nearly limited to the sampling passband to minimize aliasing at the cost of blurring, and the SNR is very high to permit the retrieval of small spatial detail from the extensively blurred signal. Shading the lens aperture transmittance to increase depth of field and using a regular hexagonal sensor-array instead of square lattice to decrease sensitivity to edge orientation also improves the signal information density up to about 30 percent at high SNRs.

Huck, F. O.↗
NASA NTRS1984

Characterization of the LANDSAT sensors' spatial responses

The characteristics of the thematic mapper (TM) and multispectral scanner (MSS) sensors on LANDSATs 4 and 5 affecting their spatial responses are described, and functions defining the response of the system to an arbitrary input spatial pattern are derived, i.e., transfer functions (TF) and line spread functions (LSF). These design LSF's and TF's were modified based on prelaunch component and system measurements to provide improved estimates. Prelaunch estimates of LSF/FT's are compared to in-orbit estimates. For the MSS instruments, only limited prelaunch scan direction square-wave response (SWR) data were available. Design estimates were modified by convolving in Gaussian blur till the derived LSF/TF's produced SWR's comparable to the measurements. The two MSS instruments were comparable at their temperatures of best focus; separate calculations were performed for bands 1 and 3, band 2 and band 4. The pre-sample nadir effective instantaneous field's of view (EIFOV's) based on the .5 modulation transfer function (MTF) criteria vary from 70 to 75 meters in the track direction and 79 to 82 meters in the scan direction. For the TM instruments more extensive prelaunch measurements were available. Bands 1 to 4, 5 and 7, and 6 were handled separately as were the two instruments. Derived MTF's indicate nadir pre-sample EIFOV's of 32 to 33 meter track (bands 1 to 5, 7) and 36 meter scan (bands 1 to 5, 7) and 1245 meter track (band 6) and 141 meter scan (band 6) for both TM's.

Markham, B. L.↗
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.↗