Retinex by Higher Order Total Variation \(L^{1}\) Decomposition
In this paper, we propose a reflectance and illumination decomposition model for the Retinex problem via high-order total variation and \(L^{1}\) decomposition. Based on the observation that illumination varies smoother than reflectance, we propose a convex variational model which can effectively decompose the gradient field of images into salient edges and relatively smoother illumination field through the first- and second-order total variation regularizations. The proposed model can be efficiently solved by a primaldual splitting method. Numerical experiments on both grayscale and color images show the strength of the proposed model with applications to Retinex illusions, medical image bias field removal, and color image shadow correction.
retinex high-order total variation shadow correction image decomposition