A Novel Retinex-Based Fractional-Order Variational Model for Image with Severely Low Light
In this paper, we propose a novel Retinex-based fractional-order variational model for severely low-light images. The proposed method is more flexible in controlling the reg- ularization extent than the existing integer-order regularization methods. Specifically, we decompose directly in the image domain and perform the fractional-order gradient total variation regu- larization on both the reflectance component and the illumination component to get more appropriate estimated results. The merits of the proposed method are as follows: 1) small-magnitude details are maintained in the estimated reflectance. 2) illumination com- ponents are effectively removed from the estimated reflectance. 3) the estimated illumination is more likely piecewise smooth. We compare the proposed method with other closely related Retinex-based methods. Experimental results demonstrate the effectiveness of the proposed method
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