An Intensity Separated Variational Regularization Model for Multichannel Image Enhancement

Abstract

The channels of the multi-temporal SAR image have strong scattering target distribution in different positions. Focus on this, this paper propose the intensity segregation representation model for the multi-temporal SAR image restoration. This new variational regularization model based on the intensity separation of the multi-temporal SAR image is composed of two sub-models. The first one is a variational regularization model for the intensity component of the image, where the noise is assumed to be multiplicative, and the regularization term is the total variation. A fixed point iterative algorithm is used to solve the Euler-Lagrangian equation of the first sub-model. The second sub-model is the vectorial variational regularization model for the vector component of the image, which is obtained by the assumption that the noise is multiplicative. And the vectorial total variation norm of the vector defined on the unit sphere is obtained. A partial differential equation method is used to get the differential iterative algorithm to solve the Euler-Lagrangian equation of the second sub-model. In this paper, the intensity separation model is applied to the multi-temporal SAR image despeckling. The strong scattering target is well preserved while the good efficient of despeckling is obtained. In summary, this method is proved to highly promote the ability of distinguish different kinds of surface target of the multi-temporal SAR image.