Structure tensor adaptive total variation for image restoration

Authors

SURYA PRASATH
DANG NH THANH

DOI

10.3906/elk-1802-76

Abstract

Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.

Keywords

Image restoration, total variation, adaptive, structure tensor, inverse gradient

First Page

1147

Last Page

1156

Recommended Citation

PRASATH, SURYA and THANH, DANG NH (2019) "Structure tensor adaptive total variation for image restoration," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27: No. 2, Article 35. https://doi.org/10.3906/elk-1802-76
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss2/35