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

R. Bruce, C. Ruet, M. Devlin, S. Marshall

Research output: Contribution to conferencePaper

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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.
Original languageEnglish
Number of pages27
Publication statusPublished - 2007
Event3rd Manchester Reconfigurable Supercomputing Conference - Manchester, United Kingdom
Duration: 28 Mar 200729 Mar 2007


Conference3rd Manchester Reconfigurable Supercomputing Conference
Abbreviated titleMRSC 2007
CountryUnited Kingdom


  • star-formation
  • star-field images
  • floating-point cost function
  • convolution
  • high-level languages
  • core libraries

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