An adaptive strategy for the restoration of textured images using fractional order regularization. (English) Zbl 1289.68196

Chan, R. H.; Lanza, A.; Morigi, S.; Sgallari, F.

Numer. Math., Theory Methods Appl. 6, No. 1, 276-296 (2013).

Summary: Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.

MSC:

68U10	Computing methodologies for image processing
65F22	Ill-posedness and regularization problems in numerical linear algebra
65F10	Iterative numerical methods for linear systems

Keywords:

ill-posed problem; deblurring; fractional-order derivatives; regularizing iterative method

Software:

FTVd; Regularization tools