Restoration of star-field images using high-level languages and core libraries

Bruce, R. and Ruet, C. and Devlin, M. and Marshall, S. (2007) Restoration of star-field images using high-level languages and core libraries. In: 3rd Manchester Reconfigurable Supercomputing Conference, 2007-03-28 - 2007-03-29.

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      Abstract

      Research into the use of FPGAs in Image Processing began in earnest at the beginning of the 1990s. Since then, many thousands of publications have pointed to the computational capabilities of FPGAs. During this time, FPGAs have seen the application space to which they are applicable grow in tandem with their logic densities. When investigating a particular application, researchers compare FPGAs with alternative technologies such as Digital Signal Processors (DSPs), Application-Specific Integrated Cir-cuits (ASICs), microprocessors and vector processors. The metrics for comparison depend on the needs of the application, and include such measurements as: raw performance, power consumption, unit cost, board footprint, non-recurring engineering cost, design time and design cost. The key metrics for a par-ticular application may also include ratios of these metrics, e.g. power/performance, or performance/unit cost. The work detailed in this paper compares a 90nm-process commodity microprocessor with a plat-form based around a 90nm-process FPGA, focussing on design time and raw performance. The application chosen for implementation was a minimum entropy restoration of star-field images (see [1] for an introduction), with simulated annealing used to converge towards the globally-optimum solution. This application was not chosen in the belief that it would particularly suit one technology over another, but was instead selected as being representative of a computationally intense image-processing application.