A Color-Based Selective and Interactive Filter Using Weighted TV

In this paper we propose to introduce a new color interactive and selective filtering tool based on the minimization of a weighted vectorial total variation term (

TV

g

) with the

L

2

norm as data term. Our goal is to filter one region of an image while preserving the other. To this end, we introduce color moments in the

TV

g

term. Up to now, color moments have been mainly introduced for indexation purposes. In our case, the user selects some points in the area to preserve and some other points in the area to be filtered. Reference color moments are then computed on patches around the selected points and are included in the

TV

g

term. Two main functions

g

are tested within the HSL color space leading to interesting results on both synthetic and real images. Convex optimization tools are used to solve the minimization issue and we take benefit of an augmented Lagrangian formulation and Uzawa block relaxation schemes. The proposed filtering tool may be interesting as a pre-processing step for segmentation, movie post-production or object-oriented compression.