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Nonlinear total variation based noise removal algorithms (1992)
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BibTeX
@MISC{Rudin92nonlineartotal,
author = {Leonid I. Rudin and Stanley Osher and Emad Fatemi},
title = {Nonlinear total variation based noise removal algorithms},
year = {1992}
}
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Abstract
A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t--- ~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.
Keyphrases
nonlinear total variation noise removal algorithm numerical algorithm total variation constrained optimization type second step noisy image level set sharp edge first step gradient-projection method denoised image lagrange multiplier steady state time dependent partial differential equation constraint set
