Texture segmentation still constitutes an ongoing challenge, especially when processing large-size images. The aim of this work is twofold.
First, we provide a variational model for simultaneously extracting and regularizing local texture features, such as local regularity and local variance. For this purpose, a scale-free wavelet-based model, penalised by a Total Variation regularizer, is embedded into a convex optimisation framework. Second, we investigate convergence acceleration strategies, relying on strong-convexity of the objective function, in order to deal with computational cost induced by the minimization. Finally, we illustrate the developed procedures on real-world images of multiphasic flows.