Edge-Preserving Image Reconstruction with Wavelet-Domain Edge Continuation
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Nonparametric cepstrum estimation via optimal risk smoothing
IEEE Transactions on Signal Processing
Automatic parameter selection for denoising algorithms using a no-reference measure of image content
IEEE Transactions on Image Processing
A Bias-Variance Approach for the Nonlocal Means
SIAM Journal on Imaging Sciences
Anisotropic non-local means with spatially adaptive patch shapes
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
Journal of Mathematical Imaging and Vision
Total variation regularization algorithms for images corrupted with different noise models: a review
Journal of Electrical and Computer Engineering
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We consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white Gaussian noise. To achieve this, we propose to minimize Stein's unbiased risk estimate (SURE) which provides a means of assessing the true mean-squared error (MSE) purely from the measured data without need for any knowledge about the noise-free signal. Specifically, we present a novel Monte-Carlo technique which enables the user to calculate SURE for an arbitrary denoising algorithm characterized by some specific parameter setting. Our method is a black-box approach which solely uses the response of the denoising operator to additional input noise and does not ask for any information about its functional form. This, therefore, permits the use of SURE for optimization of a wide variety of denoising algorithms. We justify our claims by presenting experimental results for SURE-based optimization of a series of popular image-denoising algorithms such as total-variation denoising, wavelet soft-thresholding, and Wiener filtering/smoothing splines. In the process, we also compare the performance of these methods. We demonstrate numerically that SURE computed using the new approach accurately predicts the true MSE for all the considered algorithms. We also show that SURE uncovers the optimal values of the parameters in all cases.