Asymptotically optimal blind estimation of multichannel images

Ian Atkinson, Farzad Kamalabadi, Satish Mohan, Douglas L. Jones

Research output: Contribution to journalArticlepeer-review

Abstract

Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the required second-order signal and noise statistics are not known in which the decorrelation is difficult or expensive to calculate. An asymptotically optimal estimation scheme proposed here uses a 2-D discrete wavelet transform to approximately decorrelate the signal in space and the discrete Fourier transform to decorrelate between channels. The coefficient weighting is replaced with a wavelet-domain thresholding operation to result in an efficient estimation scheme for both stationary and nonstationary signals. In contrast to optimal estimation, this new scheme does not require second-order signal statistics, making it well suited to many applications. In addition to providing vastly improved visual quality, the new estimator typically yields signal-to-noise ratio gains 12 dB or higher for hyperspectral imagery and functional magnetic resonance images.

Original languageEnglish (US)
Pages (from-to)992-1007
Number of pages16
JournalIEEE Transactions on Image Processing
Volume15
Issue number4
DOIs
StatePublished - Apr 2006

Keywords

  • Estimation
  • Image processing
  • Multidimensional signal processing
  • Wavelet transforms

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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