A Novel Retinex-Based Fractional-Order Variational Model for Images 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 regularization extent than the existing integer-order regularization methods. Specifically, we decompose directly in the image domain and perform the fractional-order gradient total variation regularization 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 components 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.