A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise

Abstract EN

This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise.

Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term.

Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition.

Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator.

We also study some mathematical properties of the proposed model.

In addition, the model is solved by applying the primal-dual algorithm.

The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.