Image decomposition into structure and texture subcomponents with multifrequency modulation constraints

Texture information in images is coupled with geometric macrostructures and piecewise-smooth intensity variations. Decomposing an image f into a geometric structure component u and a texture component v is an inverse estimation problem, essential for understanding and analyzing images depending on their content. In this paper, we present a novel combined approach for simultaneous texture from structure separation and multiband texture modeling. First, we formulate a new, variational decomposition scheme, involving an explicit texture reconstruction constraint (prior) formed by the responses of selected frequency-tuned linear filters. This forms a dasiau + Kvpsila image model of K + 1 components. Subsequent texture modeling is applied to the estimated v component and its consistency is compared to using the complete, initial image f. The decomposition step, functioning as an advanced texture-front end, improves clustering and classification performance, for various multiband features. The proposed method can be generalized to other texture models or applications.