A two-stage denoising filter: The preprocessed Yaroslavsky filter
Joseph Salmona b, Rebecca M. Willetta b, Ery Arias-castro Ac d
- aDepartment of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- bDepartment of Electrical & Computer Engineering, Duke University, Durham, NC, USA
- cDepartment of Mathematics, University of California, San Diego, CA, USA
- dDept. of Mathematics, University of California, San Diego, USA
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Abstract
This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky filter for strong numerical, visual, and theoretical performance on a broad class of images. The framework developed is a two-stage approach. In the first stage the image is denoised by a classical denoising method (e.g., wavelet or curvelet thresholding). In the second step a modification of the Yaroslavsky filter is performed on the original noisy image, where the weights of the filters are governed by pixel similarities in the denoised image from the first stage. The procedure is supported by theoretical guarantees, achieves very good performance for cartoon images, and can be computed much more quickly than current patch-based denoising algorithms.