Scale-Aware Guided and Structure-Preserved Texture Filter

In this letter, a new texture filter with scale-aware gradients and structural preservation is proposed. The proposed filter uses a hybrid $L_{0}$ - $H^{-1} $ variational model via measuring sparsity with scale-aware gradients by the $L_{0}$ norm and structural fidelity by the $H^{-1}$ norm with the embedded Laplacian operator. Extensive qualitative and quantitative experimental results demonstrate that the proposed method 1) smooths small-scale low/high contrast textures and intensive noise while preserving sharp and prominent structures simultaneously; 2) significantly outperforms state-of-the-art texture filtering methods; and 3) has fast convergence.