Combining local regularity estimation and total variation optimization for scale-free texture segmentation
This work addresses texture segmentation for image processing applications, presenting an incremental improvement by integrating existing techniques to solve a specific bottleneck.
The paper tackled the problem of segmenting scale-free textures by combining local regularity estimation using wavelet leaders with total variation optimization to address the bias-variance trade-off, achieving quantified performance improvements on synthetic and real-world textures.
Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures.