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Context-based denoising of images using iterative wavelet thresholding

: Marpe, D.; Cycon, H.L.; Zander, G.; Barthel, K.-U.


Jay Kuo, C.-C. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Visual Communications and Image Processing 2002. Vol. 2 : 21 - 23 January 2002, San Jose, USA
Bellingham/Wash.: SPIE, 2002 (SPIE Proceedings Series 4671)
ISBN: 0-8194-4411-1
Conference "Visual Communications and Image Processing" <2002, San Jose/Calif.>
Conference Paper
Fraunhofer HHI ()
discrete wavelet transforms; image reconstruction; image restoration; iterative methods; mean square error methods; image denoising; iterative wavelet thresholding; spatially adaptive wavelet thresholding; context model; weighted variance estimation; predominant correlations; reliable statistical estimation; context-based thresholding; visual quality; mean squared error; mse; discrete wavelet transform

In this paper, we propose a spatially adaptive wavelet thresholding method using a context model that has been inspired by our prior work on image coding. The proposed context model relies on an estimation of the weighted variance in a local window of scale and space. Appropriately chosen weights are used to model the predominant correlations for a reliable statistical estimation. By iterating the context-based thresholding operation, a more accurate reconstruction can be achieved. Experimental results show that our proposed method yields significantly improved visual quality as well as lower mean squared error compared to the best recently published results in the denoising literature.