We propose a variational approach for deblurring and impulsive noise removal in multi-channel images. A robust data fidelity measure and edge preserving regularization are employed. We consider several regularization approaches, such as Beltrami flow, Mumford-Shah and Total-Variation Mumford-Shah. The latter two methods are extended to multi-channel images and reformulated using the Γ-convergence approximation. Our main contribution is in the unification of image deblurring and impulse noise removal in a multi-channel variational framework. Theoretical and experimental results show that the Mumford-Shah and Total Variation Mumford Shah regularization methods are superior to other color image restoration regularizers. In addition, these two methods yield a denoised edge map of the image.
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