Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion

We propose a unified framework for isolating, comparing, and differentiating objects within an image. We rely on the recently proposed total-variation transform, yielding a continuous, multi-scale, fully edge-preserving, and local descriptor, referred to as spectral total-variation local scale signatures. We show and analyze several useful merits of this framework. Signatures are sensitive to size, local contrast, and composition of structures; are invariant to translation, rotation, flip, and linear illumination change; and texture signatures are robust to the underlying structures. We prove exact conditions in the 1D case. We propose several applications for this framework: saliency map extraction for fusion of thermal and optical images or for medical imaging, clustering of vein-like features, and size-based image manipulation.