  We present a new vectorial total variation method that addresses the problem
of color consistent image filtering. Our approach is inspired from the
double-opponent cell representation in the human visual cortex. Existing
methods of vectorial total variation regularizers have insufficient (or no)
coupling between the color channels and thus may introduce color artifacts. We
address this problem by introducing a novel coupling between the color channels
related to a pullback-metric from the opponent space to the data (RGB color)
space. Our energy is a non-convex, non-smooth higher-order vectorial total
variation approach and promotes color consistent image filtering via a coupling
term. For a convex variant, we show well-posedness and existence of a solution
in the space of vectorial bounded variation. For the higher-order scheme we
employ a half-quadratic strategy, which model the non-convex energy terms as
the infimum of a sequence of quadratic functions. In experiments, we elaborate
on traditional image restoration applications of inpainting, deblurring and
denoising. Regarding the latter, we demonstrate state of the art restoration
quality with respect to structure coherence and color consistency.
