A Perceptually Inspired Variational Method for the Uneven Intensity Correction of Remote Sensing Images

Perceptually inspired color correction methods are characterized by human visual system properties. In this paper, we propose a perceptually inspired variational method for uneven intensity correction of remote sensing images. The proposed method shares the same intrinsic scheme as the Retinex theory, but the reflectance in this method is solved directly within the limited dynamic range and is supposed to comply with the gray world assumption. Considering the smoothness of illumination and the complexity of reflectance, the proposed method integrates L2 norm and total variation prior to inflict varying constraints to different components and regions. The minimum of this variational model is calculated using the steepest descent approach. Experimental results are presented to validate the effectiveness of the proposed method.

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