2D FFT With Simultaneous Edge Artifact Removal (No. 0084)

 
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Summary

Image processing algorithm that simultaneously removes edge artifacts in real time.

The growth of diagnostic imaging market is primarily driven by the increasing demand for early disease diagnosis and widening scope of clinical applications. 2D Fast Fourier Transforms (FFTs) have become a computational constraint for real-time/near real-time systems. FFTs inherently assume that image edges are periodic leading to high amplitude “cross-shaped” artifacts in the frequency domain. These artifacts can be propagated to later stages of processing, adversely affecting decision critical applications, such as, medical diagnostics. Here, we present a promising imaging algorithm developed by a group of researchers led by Prof. Ulf Skoglund. The developed algorithm simultaneously removes edge artifacts by decomposing the image into periodic and smooth components in real-time.

 

Lead Researcher:
Ulf Skoglund

Faculty of Structural Cellular Biology Unit

Applications

  • High speed industrial tracking
  • Medical diagnostics (MRI, CT, etc.)
  • Electron microscopy
  • Astronomical imaging
  • Image processing (convolution)

 

Advantages

  • Real-time processing
  • Minimization of artifacts
  • 100 fps for 2048 x 2048 pixel image

     Click on the images to enlarge

Technology's Essence

A novel algorithm for performing 2-dimensional discrete Fourier transform of a subject image data to be performed in one or more digital processors includes performing 1-dimensional FFT on each row of the subject image data and 1-dimensional FFT on each column of the subject image, and performing a simplified FFT processing on the extracted boundary image without performing column-by-column 1-dimensional FFT by: performing 1-dimensional FFT only on a first column vector in the extracted boundary image data, using scaled column vectors to derive FFT of remaining columns of the extracted boundary image data, and performing 1-dimensional FFT on each row of the extracted boundary image data. Then, FFT of a periodic component of the subject image data with edge-artifacts removed and FFT of a smooth component of the subject image data are derived from results of the steps.

 

Media Coverage and Presentations

 JST Technology Showcase Presentation

 

CONTACT FOR MORE INFORMATION

  Graham Garner
Technology Licensing Section

  tls@oist.jp
  +81(0)98-966-8937