Confidence driven TGV fusion

TL;DR

This paper presents a new confidence-driven variational data fusion model that jointly estimates data and confidence values, improving depth image fusion through spatial coherence and biconvex optimization.

Contribution

The paper introduces a novel confidence-driven variational model for data fusion that jointly estimates data and confidence, with algorithms and convergence analysis.

Findings

Effective depth image fusion demonstrated on synthetic data.

Model leverages spatial coherence for improved results.

Algorithms show convergence for the biconvex problem.

Abstract

We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values. The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data. We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimization problem and their convergence. The performance of the proposed model is evaluated considering the problem of depth image fusion by using both synthetic and real data from publicly available datasets.