The acquisition process of remote sensing images is influenced by many factors

such as the internal turbulences of the sensor and the external environmental variations

which causes degradations in the observed image. The uneven intensity distribution is a typical degradation caused by internal and external factors

directly related to the decrease of the accuracy of the interpretation and applications of remote sensing images. Therefore

the correction of uneven intensity is necessary for improving the quality of remote sensing images. The perceptually inspired variational method(PIVM) for the uneven intensity correction is a novel method to correct the uneven intensity of a single remote sensing image. PIVM can effectively correct the overall brightness of the image while enhancing the local contrast

which is inspired by the human visual system properties. In this paper

the split Bregman algorithm is used to optimize this variational model. The model is composed of two kinds of priors: the total variation (TV) and the L. The combination model can be split into two sub problems

which can increase the computational efficiency while ensuring the even intensity. Experiments on synthetic and real remote sensing images are taken to validate the split Bregman based PIVM. Results show that the uneven intensity can be effectively corrected by the proposed split Bregman method

and meanwhile the global color and local contrast are satisfied. Moreover

the running time of the split Bregman algorithm is one seventh to one sixth of that of the traditional steepest decent method. The split Bregman based PIVM is an efficient and effective method for correcting uneven intensity in remote sensing images. It provides the opportunity to extend the PIVM to the application on large scenes.