Abstract

In this paper, we propose an efficient higher order total variation regularization scheme for image denoising problem. By relaxing the constraints appearing in the traditional infimal convolution regularization, the proposed higher order total variation can remove the staircasing effects caused by total variation as well as preserve sharp edges and finer details well in the restored image. We characterize the solution of the proposed model using fixed point equations (via the proximity operator) and develop convergent proximity algorithms for solving the model. Our numerical experiments demonstrate the efficiency of the proposed method.

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