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

Software for the Standard Linear Format for Digital Cartographic Feature Data

This paper describes application software and programming tools designed for use with the Defense Mapping Agency\u27s (DMA) Standard Linear Format (SLF) for Digital Cartographic Feature Data. The Standard Linear Format (SLF) is briefly described in this report. It was designed as a standard for the exchange of digital cartographic features on magnetic tape. The format specifies descriptive fields about feature data, as well as specifying the representation of the features. The application software described in this report can transfer files or tapes in this format to relational database maintained under Ingres, graph features in the database, alter the database, and convert the database back to the SLF. The subroutine library utilized by these application programs is also described. These subroutine should make the task of writing additional application software for the SLF much easier

Small-kernel image restoration

The goal of image restoration is to remove degradations that are introduced during image acquisition and display. Although image restoration is a difficult task that requires considerable computation, in many applications the processing must be performed significantly faster than is possible with traditional algorithms implemented on conventional serial architectures. as demonstrated in this dissertation, digital image restoration can be efficiently implemented by convolving an image with a small kernel. Small-kernel convolution is a local operation that requires relatively little processing and can be easily implemented in parallel. A small-kernel technique must compromise effectiveness for efficiency, but if the kernel values are well-chosen, small-kernel restoration can be very effective.;This dissertation develops a small-kernel image restoration algorithm that minimizes expected mean-square restoration error. The derivation of the mean-square-optimal small kernel parallels that of the Wiener filter, but accounts for explicit spatial constraints on the kernel. This development is thorough and rigorous, but conceptually straightforward: the mean-square-optimal kernel is conditioned only on a comprehensive end-to-end model of the imaging process and spatial constraints on the kernel. The end-to-end digital imaging system model accounts for the scene, acquisition blur, sampling, noise, and display reconstruction. The determination of kernel values is directly conditioned on the specific size and shape of the kernel. Experiments presented in this dissertation demonstrate that small-kernel image restoration requires significantly less computation than a state-of-the-art implementation of the Wiener filter yet the optimal small-kernel yields comparable restored images.;The mean-square-optimal small-kernel algorithm and most other image restoration algorithms require a characterization of the image acquisition device (i.e., an estimate of the device\u27s point spread function or optical transfer function). This dissertation describes an original method for accurately determining this characterization. The method extends the traditional knife-edge technique to explicitly deal with fundamental sampled system considerations of aliasing and sample/scene phase. Results for both simulated and real imaging systems demonstrate the accuracy of the method

Information efficiency in hyperspectral imaging systems

In this work we develop a method for assessing the information density and efficiency of hyperspectral imaging systems that have spectral bands of non-uniform width. Imaging system designs with spectral bands of nonuniform width can efficiently gather information about a scene by allocating bandwidth among the bands according to their information content. The information efficiency is the ratio of information density to data density and is a function of the scene’s spectral radiance, hyperspectral system design, and signal-to-noise ratio. The assessment can be used to produce an efficient system design. For example, one approach to determining the number and width of the spectral bands for an information- efficient design is, to begin with a design that has a single band and then to iteratively divide a band into two bands until no further division improves the system’s efficiency. Two experiments illustrate this approach, one using a simple mathematical model for the scene spectral-radiance autocorrelation function and the other using the deterministic spectral-radiance autocorrelation function of a hyperspectral image from NASA’s Advanced Solid-State Array Spectroradiometer. The approach could be used either to determine a fixed system design or to dynamically control a system with variable-width spectral bands (e.g., using on-board processing in a satellite system)

Müller cell activation, proliferation and migration following laser injury.

PurposeMüller cells are well known for their critical role in normal retinal structure and function, but their reaction to retinal injury and subsequent role in retinal remodeling is less well characterized. In this study we used a mouse model of retinal laser photocoagulation to examine injury-induced Müller glial reaction, and determine how this reaction was related to injury-induced retinal regeneration and cellular repopulation.MethodsExperiments were performed on 3-4-week-old C57BL/6 mice. Retinal laser photocoagulation was used to induce small, circumscribed injuries; these were principally confined to the outer nuclear layer, and surrounded by apparently healthy retinal tissue. Western blotting and immunohistochemical analyses were used to determine the level and location of protein expression. Live cell imaging of green fluorescent protein (GFP)-infected Müller cells (AAV-GFAP-GFP) were used to identify the rate and location of retinal Müller cell nuclear migration.ResultsUpon injury, Müller cells directly at the burn site become reactive, as evidenced by increased expression of the intermediate filament proteins glial fibrillary acidic protein (GFAP) and nestin. These reactive cells re-enter the cell cycle as shown by expression of the markers Cyclin D1 and D3, and their nuclei begin to migrate toward the injury site at a rate of approximately 12 microm/hr. However, unlike other reports, evidence for Müller cell transdifferentiation was not identified in this model.ConclusionsRetinal laser photocoagulation is capable of stimulating a significant glial reaction, marked by activation of cell cycle progression and retinal reorganization, but is not capable of stimulating cellular transdifferentiation or neurogenesis