Patch-Based Near-Optimal Image Denoising

In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for...

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Bibliographic Details
Published in:IEEE transactions on image processing 2012-04, Vol.21 (4), p.1635-1649
Main Authors: Chatterjee, P., Milanfar, P.
Format: Article
Language:English
Subjects:
Algorithms
Covariance matrix
Denoising bounds
image clustering
Image denoising
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
linear minimum mean-squared-error (LMMSE) estimator
Nickel
Noise
Noise measurement
Noise reduction
Photography - methods
Redundancy
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Wiener filter
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Summary:In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm. We propose a patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters. We describe how these parameters can be accurately estimated directly from the input noisy image. Our denoising approach, designed for near-optimal performance (in the mean-squared error sense), has a sound statistical foundation that is analyzed in detail. The performance of our approach is experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is on par or exceeding the current state of the art, both visually and quantitatively.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2172799