A real time computer image generation system incorporating comprehensive distortion correction in which a predistorted image is computed and placed on a projection raster prior to projection onto a view screen in order that the image as seen by a viewer appears correct A method for implementing comprehensive distortion correction in real time utilizes fixed transfer characteristics of a projection lens coupled with scene by scene translation of each of a plurality of predetermined points from a projection raster to a location on a view screen Each image is divided into a plurality of spans and the span corners are mapped from the projection raster to the view screen The location of image vertices on the view screen are determined with respect to the viewer and the vertices are mapped to corresponding locations on the projector raster An image is then generated by locating the spans within which each of the vertices is positioned and constructing an image on the projection raster using the map span corners in viewer space and the mapped vertices in projector space The reconstructed predistorted image is then projected onto the view screen so that it appears correct to a viewer

Comprehensive distortion correction in a real time imaging system

Investigation of the image resampling requirements of remote sensing has indicated a need for improved resampling convolution kernel design. Areas in which progress has been made include a recognition of the improved phase linearity of longer kernels and the need for similarity of the modulation transfer function (MTF) across all filters. The computational capability required for the longer kernels is achieved with a dedicated signal processor.

High-fidelity Image Resampling For Remote Sensing

Band-to-band coregistration of multispectral remote sensing images can be achieved by electronic signal processing techniques rather than by costly and difficult mechanical alignment. This paper describes the results of a study of the end-to-end performance of electronic registration. The software simulation includes steps which model the performance of the geometric calibration process, the instrument image quality, detector performance and the effects of achieving coregistration through image resampling. The image resampling step emulates the Pipelined Resampling Processor, a real-time image resampler. The study demonstrates that the electronic alignment technique produces multispectral images which are superior to those produced by an imager whose pixel geometry is accurate to 0.1 pixel rms. The implications of this approach for future earth observation programs are discussed.

Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

The Pipelined Resampling Processor (PRP) was presented to this conference in 1983 in concept as a practical solution to real-time high resolution geometric image rectification needs. In the present report we present performance results obtained from the PRP development unit completed in late 1983. The small size, weight, and power requirements of the PRP and its high throughput make it very well suited for space and airborne applications where goemetric correction of image data must be done autonomously in real-time. This high resolution geometric correction is a necessary adjunct to applications using frame differencing or frame averaging for motion compensation, moving target indication, noise suppression or data compression as well as applications requiring precise correction of focal plane sampling distortions. By equipping the PRP with manual controls, image geometry can be manipulated at video rates from a console to achieve a much higher image analysis throughput than is possible for more general purpose processing facilities. In mapping, merging, classifi-cation and registration applications, interactive video rate processing will be important in bringing these and other image analysis techniques out of the laboratory and into an operational environment.

The Pipelined Resampling Processor: Performance Of A Development Unit Which Is Applicable To A Wide Range Of Real-Time Imaging Applications

: Dual Difference Filtering (DDF), a symmetric signal processor developed as a means of detecting changes in a pair of displaced digital signals, supplants interpolation and subtraction. For oversampled signals, DDF performance can be described by an error spectrum that is the product of the spectrum of the underlying continuous signal and an attenuation factor that depends only on the filters. For undersampled or stochastic signals, performance is similarly describable, but in an average sense. Simple formulas are found for the attenuation factors in terms of the filters' real or frequency space descriptions. A fundamental equation for error minimization is also derived that is useful when the form of the power spectrum is known. Optimal DDFs are computed for a power-law spectrum that is appropriate for imagery. Also, flexible suboptimal design methods are developed that produce superior performance for a wide range of signal spectra. Typical signal to clutter improvements of 30 dB relative to standard interpolation/subtraction methods are demonstrated.... Change detection, Interpolation, Digital Filtering, Linear estimation, Nyquist sampling, Moving target indication, Image processing.

Dual Difference Filtering: A Replacement for Interpolation and Subtraction to Detect Changes in Misregistered Signals

A new technique has been developed for geometrically rectifying images to subpixel accuracy in real time through high fidelity resampling. This technique permits significant simplification and cost reduction for a remote sensing system by relaxing the optical/mechanical alignment tolerances by about two orders of magnitude. A pipelined resampling processor has been built whose small size, weight, and power consumption permit it to be used on board a spacecraft or other vehicle. The high throughput of the processor prevents data logjams and permits the use of resampling kernels with better MTF and phase linearity than previously practical. A new design criterion that produces improved uniformity of MTF in the final image has been identified for the resampling kernels. These improvements over conventional resampling techniques result in improved data quality delivered to the end user and offer opportunities for streamlined data distribution. This technique and processor can also be applied to image motion compensation, data set merging, man-in-the-loop image warping, and image motion generation.

Electronic Image Alignment: Implementation And Applications To Imaging System Design

A hardware architecture for real-time image resampling has been developed which will support a wide range of image resampling tasks arising in remote sensing applications. In particular, local spatial sampling errors caused by misalignment of sensor arrays and optical defects, plus global spatial sampling errors caused by platform pointing errors, can be simultaneously rectified. This is achieved by referring all sample errors to the same fixed ideal sampling coordinate frame This has the added advantage of providing automatic co-registration of images obtained in several spectral bands with separate sensor arrays. The resulting architecture is modular and flexible due to a decomposition into independent parallel structures. The use of very large scale integrated circuits for memories and mul-tiplier/accumulators results in a design with a processing speed/power ratio in excess of 10° pixels per second per Watt and providing 1/16 pixel resampling accuracy.© (1984) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Allan J. Mord

Papers

High-fidelity Image Resampling For Remote Sensing

Simulation of electronic registration of multispectral remote sensing images to 0.1 pixel accuracy

The Pipelined Resampling Processor: Performance Of A Development Unit Which Is Applicable To A Wide Range Of Real-Time Imaging Applications

Electronic Image Alignment: Implementation And Applications To Imaging System Design

A Practical Architecture For A Real-Time Image Resampling Processor