Cartoon-Texture decomposition (CTD) is a fundamental task and has wide applications in image processing and computer vision. To enhance separation of the cartoon and texture, existing models explicitly introduce correlation terms to decorrelate the two components. However, existing correlations usually ignore the local geometric structure information, thus insufficient to decorrelate cartoon and texture. In this work, we propose the patch-wise cosine similarity to decorrelate the cartoon and texture. The proposed decorrelation term takes the local geometric information into account and is more effective in separating cartoon and texture. Combining our decorrelation term with the regularities for cartoon (Relative Total Variation (RTV)) and texture (div(L1)-norm), we propose a new CTD model. Extended experiments show that the proposed model outperforms existing methods in CTD, especially in preserving edges of the cartoon.
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