Multiscale texture separation

Abstract

In this paper, we investigate theoretically the behavior of Meyer's image cartoon + texture decomposition model. Our main results is a new theorem which shows that, by combining the decomposition model and a well chosen Littlewood-Paley filter, it is possible to extract almost perfectly a certain class of textures. This theorem leads us to the construction of a parameterless multiscale texture separation algorithm. Finally, we propose to extend this algorithm into a directional multiscale texture separation algorithm by designing a directional Littlewood-Paley filter bank. Several experiments show the efficiency of the proposed method both on synthetic and real images.

References in corpus (2)

• Properties of BV-G structures + textures decomposition models. Application to road detection in satellite images
• Noisy image decomposition: a new structure, texture and noise model based on local adaptivity

Cited by in corpus (4)

• Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
• 2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis
• Separation Surfaces in the Spectral TV Domain for Texture Decomposition
• Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition