New optimization scheme for L2-norm total variation semi-supervised image soft labeling

In image/video context processing, such as clustering, matting, or further editing and context-aware enhancement, the probability model on the basis of Markov property is usually employed, where the neighbors around the center have stronger connection. To realize the optimization of such probability models encounters to solve a large linear system under the objective functional of L2-norm total variation (TV). The existing feasible methods can deal with the problems with small or very large neighborhood, but there lacks of feasible method for solving linear system with intermediate neighborhood in an efficient and accurate way. In this paper, based on the theoretical analysis, we transform the optimization problem to a process with accumulated joint bilateral filtering. Both efficiency and accuracy are achieved with appropriate prove of validation. Finally, taking image soft segmentation as an example, the proposed optimization scheme is implemented on GPU with existing fast bilateral filter to show the feasibility.