High-Order Total Variation-Based Image Restoration with Spatially Adapted Parameter Selection

In this paper, we propose a high-order total variation model to restore blurred and noisy images with spatially adapted regularization parameter selection. The proposed model can substantially reduce the staircase effect, while preserving sharp jump discontinuities (edges) in the restored images. We employ an alternating direction minimization method for the proposed model. Some numerical results are given to illustrate the effectiveness of the proposed method.

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