Abstract
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard ℓ2 data fidelity term with an ℓ1 norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the ℓ2-TV ℓ1-TV and ℓ2-TV problems as special cases. This method offers competitive computational performance for ℓ2-TV and is comparable to or faster than any other ℓ1-TV algorithms of which we are aware.
| Original language | English |
|---|---|
| Pages (from-to) | 948-951 |
| Number of pages | 4 |
| Journal | IEEE Signal Processing Letters |
| Volume | 14 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2007 |
| Externally published | Yes |
Keywords
- Image restoration
- Inverse problems
- Regularization
- Total variation
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