We propose an adaptive norm strategy designed for the re-storation of images contaminated by blur and noise. Standard Tikhonov regularization can give good results with Gaussian noise and smooth images, but can over-smooth the output. On the other hand, L 1-TV (Total Variation) regularization has superior performance with some non-Gaussian noise and controls both the size of jumps and the geometry of the object boundaries in the image but smooth parts of the recovered images can be blocky. According to a coherence map of the image which is obtained by a threshold structure tensor, and can detect smooth regions and edges in the image, we apply L 2-norm or L 1-norm regularization to different parts of the image. The solution of the resulting minimization problem is obtained by a fast algorithm based on the half-quadratic technique recently proposed in  for L 1-TV regularization. Some numerical results show the effectiveness of our adaptive norm image restoration strategy.
Bertaccini, D., Chan, R., Morigi, S., & Sgallari, F. (2012). An Adaptive Norm Algorithm for Image Restoration. In A.M. Bruckstein et al. (a cura di), Scale Space and Variational Methods in Computer Vision (pp. 194-205). Springer-Verlag.
|Autori:||Bertaccini, D; Chan, R; Morigi, S; Sgallari, F|
|Titolo:||An Adaptive Norm Algorithm for Image Restoration|
|Data di pubblicazione:||2012|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-642-24785-9_17|
|Settore Scientifico Disciplinare:||Settore MAT/08 - Analisi Numerica|
|Tipo:||Capitolo o saggio|
|Altre informazioni significative:||Referee anonimi (anonymous referees)|
|Tipologia:||Contributo in libro|
|Citazione:||Bertaccini, D., Chan, R., Morigi, S., & Sgallari, F. (2012). An Adaptive Norm Algorithm for Image Restoration. In A.M. Bruckstein et al. (a cura di), Scale Space and Variational Methods in Computer Vision (pp. 194-205). Springer-Verlag.|
|Appare nelle tipologie:||03 - Contributo in libro|