Adaptive non-local level-set model for despeckling and deblurring of synthetic aperture radar imagery

ABSTRACT

In this article, we modify Mumford–Shah level-set model to handle speckles and blur in synthetic aperture radar (SAR) imagery. The proposed model is formulated using a non-local regularization framework. Hence, the model duly cares about local gradient oscillations (corresponding to the fine details/textures) during the evolution process. It is assumed that the speckle intensity is gamma distributed, while designing a maximum a posteriori estimator of the functional. The parameters of the gamma distribution (i.e. scale and shape) are estimated using a maximum likelihood estimator. The regularization parameter of the model is evaluated adaptively using these (estimated) parameters at each iteration. The split-Bregman iterative scheme is employed to improve the convergence rate of the model. The proposed and the state-of-the-art despeckling models are experimentally verified and compared using a large number of speckled and blurred SAR images. Statistical quantifiers are used to numerically evaluate the performance of various models under consideration.

Acknowledgements

Dr. Jidesh would like to thank Science and Engineering Research Board (SERB), Government of India for providing financial support under the project Grant No. ECR/2017/000230. Mr B. Balaji wishes to thank MHRD, Government of India, for providing fellowship under the Ph.D. fellowship scheme, to carryout the research activities in the Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, India.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Dr. Jidesh would like to thank Science and Engineering Research Board (SERB), Government of India for providing financial support under the project Grant Number ECR/2017/000230.