Engineering topics

Reichenbach, Stephen E.

Publications and source records attributed to Reichenbach, Stephen E..

11 records

NASA NTRS1997

Restoration and reconstruction from overlapping images

This paper describes a technique for restoring and reconstructing a scene from overlapping images. In situations where there are multiple, overlapping images of the same scene, it may be desirable to create a single image that most closely approximates the scene, based on all of the data in the available images. For example, successive swaths acquired by NASA's planned Moderate Imaging Spectrometer (MODIS) will overlap, particularly at wide scan angles, creating a severe visual artifact in the output image. Resampling the overlapping swaths to produce a more accurate image on a uniform grid requires restoration and reconstruction. The one-pass restoration and reconstruction technique developed in this paper yields mean-square-optimal resampling, based on a comprehensive end-to-end system model that accounts for image overlap, and subject to user-defined and data-availability constraints on the spatial support of the filter.

Reichenbach, Stephen E.↗

NASA NTRS1994

Compression of regions in the global advanced very high resolution radiometer 1-km data set

The global advanced very high resolution radiometer (AVHRR) 1-km data set is a 10-band image produced at USGS' EROS Data Center for the study of the world's land surfaces. The image contains masked regions for non-land areas which are identical in each band but vary between data sets. They comprise over 75 percent of this 9.7 gigabyte image. The mask is compressed once and stored separately from the land data which is compressed for each of the 10 bands. The mask is stored in a hierarchical format for multi-resolution decompression of geographic subwindows of the image. The land for each band is compressed by modifying a method that ignores fill values. This multi-spectral region compression efficiently compresses the region data and precludes fill values from interfering with land compression statistics. Results show that the masked regions in a one-byte test image (6.5 Gigabytes) compress to 0.2 percent of the 557,756,146 bytes they occupy in the original image, resulting in a compression ratio of 89.9 percent for the entire image.

Kess, Barbara L.↗

NASA NTRS1993

Visual communication - Information and fidelity

This assessment of visual communication deals with image gathering, coding, and restoration as a whole rather than as separate and independent tasks. The approach focuses on two mathematical criteria, information and fidelity, and on their relationships to the entropy of the encoded data and to the visual quality of the restored image. Past applications of these criteria to the assessment of image coding and restoration have been limited to the link that connects the output of the image-gathering device to the input of the image-display device. By contrast, the approach presented in this paper explicitly includes the critical limiting factors that constrain image gathering and display. This extension leads to an end-to-end assessment theory of visual communication that combines optical design with digital processing.

Huck, Freidrich O.↗

NASA NTRS1992

Model-based frequency response characterization of a digital-image analysis system for epifluorescence microscopy

Consideration is given to a model-based method for estimating the spatial frequency response of a digital-imaging system (e.g., a CCD camera) that is modeled as a linear, shift-invariant image acquisition subsystem that is cascaded with a linear, shift-variant sampling subsystem. The method characterizes the 2D frequency response of the image acquisition subsystem to beyond the Nyquist frequency by accounting explicitly for insufficient sampling and the sample-scene phase. Results for simulated systems and a real CCD-based epifluorescence microscopy system are presented to demonstrate the accuracy of the method.

Hazra, Rajeeb↗

NASA NTRS1991

Small convolution kernels for high-fidelity image restoration

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Reichenbach, Stephen E.↗

NASA NTRS1991

Multiresponse imaging system design for improved resolution

Multiresponse imaging is a process that acquires A images, each with a different optical response, and reassembles them into a single image with an improved resolution that can approach 1/sq rt A times the photodetector-array sampling lattice. Our goals are to optimize the performance of this process in terms of the resolution and fidelity of the restored image and to assess the amount of information required to do so. The theoretical approach is based on the extension of both image restoration and rate-distortion theories from their traditional realm of signal processing to image processing which includes image gathering and display.

Alter-Gartenberg, Rachel↗

NASA NTRS1990

Information theoretical assessment of digital imaging systems

The end-to-end performance of image gathering, coding, and restoration as a whole is considered. This approach is based on the pivotal relationship that exists between the spectral information density of the transmitted signal and the restorability of images from this signal. The information-theoretical assessment accounts for (1) the information density and efficiency of the acquired signal as a function of the image-gathering system design and the radiance-field statistics, and (2) the improvement in information efficiency and data compression that can be gained by combining image gathering with coding to reduce the signal redundancy and irrelevancy. It is concluded that images can be restored with better quality and from fewer data as the information efficiency of the data is increased. The restoration correctly explains the image gathering and coding processes and effectively suppresses the image-display degradations.

John, Sarah↗

NASA NTRS1990

Digital image gathering and minimum mean-square error restoration

Most digital image restoration algorithms are inherently incomplete because they are conditioned on a discrete-input, discrete-output model which only accounts for blurring during image gathering and additive noise. For those restoration applications where sampling and reconstruction are important, the restoration algorithm should be based on a more comprehensive end-to-end model which also accounts for the potentially important noiselike effects of aliasing and the low-pass filtering effects of interpolative reconstruction. It is demonstrated that although the mathematics of this more comprehensive model is more complex, the increase in complexity is not so great as to prevent a complete development and analysis of the associated minimum mean-square error (Wiener) restoration filter.

Park, Stephen K.↗

NASA NTRS1990

Information theoretical assessment of image gathering and coding for digital restoration

The process of image-gathering, coding, and restoration is presently treated in its entirety rather than as a catenation of isolated tasks, on the basis of the relationship between the spectral information density of a transmitted signal and the restorability of images from the signal. This 'information-theoretic' assessment accounts for the information density and efficiency of the acquired signal as a function of the image-gathering system's design and radiance-field statistics, as well as for the information efficiency and data compression that are obtainable through the combination of image gathering with coding to reduce signal redundancy. It is found that high information efficiency is achievable only through minimization of image-gathering degradation as well as signal redundancy.

Huck, Friedrich O.↗

NASA NTRS1989

Optimal focal-plane restoration

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Reichenbach, Stephen E.↗

NASA NTRS1989

Comparison of algorithms for computing the two-dimensional discrete Hartley transform

Three methods have been described for computing the two-dimensional discrete Hartley transform. Two of these employ a separable transform, the third method, the vector-radix algorithm, does not require separability. In-place computation of the vector-radix method is described. Operation counts and execution times indicate that the vector-radix method is fastest.

Reichenbach, Stephen E.↗