Name
Abstract | ||
|---|---|---|
In this paper we propose a blind deconvolution algorithm based on the total variation regularization formulated as a nonlinear inverse scale space method that allows an efficient recovery of edges and textures of blurry and noisy images. The proposed explicit scheme gives the restored image solution by evolving in time the zero signal and an estimated kernel until a stopping criterion is satisfied. Numerical results indicate that our scheme is robust and converges quickly to the solution of the model for images convolved with either a Gaussian-like experimental point spread function or Gaussian blur and contaminated with Gaussian white noise. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1137/080724289 | SIAM J. Imaging Sciences |
Keywords | Field | DocType |
image solution,efficient recovery,nonlinear inverse scale space,blind deconvolution algorithm,estimated kernel,gaussian-like experimental point spread,gaussian blur,gaussian white noise,proposed explicit scheme,total variation blind deconvolution,noisy image,denoising,total variation,scale space,blind deconvolution | Kernel (linear algebra),Noise reduction,Mathematical optimization,Blind deconvolution,Convolution,Scale space,Gaussian blur,White noise,Total variation denoising,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 1 | 1936-4954 |
Citations | PageRank | References |
10 | 0.71 | 6 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Marquina | 1 | 431 | 45.30 |