A New SURE Approach to Image
Denoising : Interscale Orthonormal
Wavelet Thresholding
Florian Luisier, Thierry Blu, Senior Member, IEEE, and
Michael Unser, Fellow, IEEE
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 3, MARCH 2007
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Outline
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Introduction
SURE Approach to Image Denoising
PSNR Comparisons and Visual Quality
Computation Time
CONCLUSION
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Introduction
• A nonredundant transform may match the
performance of redundant ones.
• Do not make any explicit hypotheses on the
clean image.
• Near-optimal performance—both regarding
quality and CPU requirement
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SURE Approach to Image
Denoising(1/5)
Our goal is to find a function that minimizes
By Stein’s Lemma and leads to
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SURE Approach to Image
Denoising(2/5)
The sensitivity of the soft-thresholding function with respect
to the value of T is high.
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SURE Approach to Image
Denoising(3/5)
Build a linearly parameterized denoising function
of the form
This linear system is solved for a by
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SURE Approach to Image
Denoising(4/5)
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SURE Approach to Image
Denoising(5/5)
The number of terms K and the parameter T can be fixed
independently of the image.
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Interscale Predictor
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Interscale Predictor
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PSNR Comparisons and Visual Quality
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PSNR Comparisons and Visual Quality
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PSNR Comparisons and Visual Quality
• 2 important criteria of judging visual quality
are widely used：
– The visibility of processing artifacts
can be reduced by taking into account intrascale dependencies
– The conservation of image edges
can be reduced by a careful consideration of interscale dependencies in
the denoising function
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Computation Time
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CONCLUSION
• Demonstrate the efficiency of our SURE-based
approach (best output PSNRs for most of the
images).
• The visual quality of our denoised images is
moreover characterized by fewer artifacts
than the other methods.
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