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where Ω is the domain of the image. It is assumed to be a convex, connected, and bounded Lipschitz open subset of R. One of the most popular regularization methods is Tikhonov regularization based on least-squares techniques with a parameter and a regularization operator. Tikhonov regularization has been widely studied in the literature; see, e.g., [5, 6, 7] and the references therein. The drawback of Tikhonov regularization is the fact that it does not preserve sharp discontinuities in the image; see [11]. To overcome this difficulty one may use a technique based on the minimization of the total variation norm subject to a noise constraint. The total variation model is well known and was first introduced in image processing by Rudin, Osher, and Fatemi in [19]. The model is referred to as the ROF model. We consider the Banach space

A conditional gradient method for primal-dual total variation-based image denoising