14,736 research outputs found
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination
We consider a variational method to solve the optical flow problem with
varying illumination. We apply an adaptive control of the regularization
parameter which allows us to preserve the edges and fine features of the
computed flow. To reduce the complexity of the estimation for high resolution
images and the time of computations, we implement a multi-level parallel
approach based on the domain decomposition with the Schwarz overlapping method.
The second level of parallelism uses the massively parallel solver MUMPS. We
perform some numerical simulations to show the efficiency of our approach and
to validate it on classical and real-world image sequences
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various matrix norms with .
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods
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