Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise. (English) Zbl 1442.94015
Summary: In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation. Total variation provides well-preserved edge features, but suffers from staircase effects in smooth regions, whereas high-order total variation can alleviate staircase effects. Moreover, we introduce a strategy for adaptively selecting regularization parameters. We develop an efficient alternating minimization algorithm for solving the proposed model. Numerical examples suggest that the proposed method has the advantages of better preserving edges and reducing staircase effects.
MSC:
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
| 65R32 | Numerical methods for inverse problems for integral equations |
Keywords:
Cauchy noise; total variation and high-order total variation; adaptive regularization parameters; alternating direction method of multipliers; image restorationSoftware:
RecPFReferences:
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