1,375 research outputs found
Confidence driven TGV fusion
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
A Non-Monotone Conjugate Subgradient Type Method for Minimization of Convex Functions
We suggest a conjugate subgradient type method without any line-search for
minimization of convex non differentiable functions. Unlike the custom methods
of this class, it does not require monotone decrease of the goal function and
reduces the implementation cost of each iteration essentially. At the same
time, its step-size procedure takes into account behavior of the method along
the iteration points. Preliminary results of computational experiments confirm
efficiency of the proposed modification.Comment: 11 page
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