COLOR IMAGE REGULARIZATION VIA CHANNEL MIXING AND HALF QUADRATIC MINIMIZATION

In this work we introduce a variational nonconvex model for color image regularization. We express the variational problem as an instance of the half quadratic algorithm (HQA). Moreover, the generalized HQA allows us to prove convergence of the variational problem. As a demonstrator of our framework, we consider a vectorial total variation (VTV) formulation with an additional nonconvex pair-wise color-channel coupling matrix. Numerical evidence show the applicability of the proposed framework compared to VTV methods and state-of-the-art image denoising methods.