Enric Meinhardt-Llopis

This work describes two anisotropic optical flow inpainting algorithms. The first one recovers the missing flow values using the Absolutely Minimizing Lipschitz Extension partial differential equation (also called infinity Laplacian equation) and the second one uses the Laplace partial differential equation, both defined on a Riemmanian manifold. The Riemannian manifold is defined by endowing the plane domain with an appropriate metric depending on the reference video frame. A detailed analysis of both approaches is provided and their results are compared on three different applications: flow densification, occlusion inpainting and large hole inpainting.

We present a detailed analysis of FALDOI, a large displacement optical flow method proposed by P.Palomares et al. This method requires a set of discrete matches, which can be extremely sparse, and an energy functional which locally guides the interpolation from the matches. It follows a two-step minimization method at the finest scale which is very robust to the outliers of the sparse matcher and can capture large displacements of small objects. The results shown in the original paper consistently outperformed the coarse-to-fine approaches and achieved good qualitative and quantitative performance on the standard optical flow benchmarks. In this paper we revise the proposed method and the changes done to significantly reduce its execution time while reporting nearly the same accuracy. Finally, we also compare it against the current state-of-the-art to assess its performance.

We propose a large displacement optical flow method that introduces a new strategy to compute a good local minimum of any optical flow energy functional. The method requires a given set of discrete matches, which can be extremely sparse, and an energy functional which locally guides the interpolation from those matches. In particular, the matches are used to guide a structured coordinate-descent of the energy functional around these keypoints. It results in a two-step minimization method at the finest scale which is very robust to the inevitable outliers of the sparse matcher and able to capture large displacements of small objects. Its benefits over other variational methods that also rely on a set of sparse matches are its robustness against very few matches, high levels of noise and outliers. We validate our proposal using several optical flow variational models. The results consistently outperform the coarse-to-fine approaches and achieve good qualitative and quantitative performance on the standard optical flow benchmarks.

You walk through a museum taking photographs of some paintings with your commodity camera. You load all these images into the computer. The computer builds a high-quality image of each one of the paintings. This article describes what the computer does. More precisely, we explain a method to produce a single, high-resolution, clean and well-lit image, out of many low-resolution noisy images taken in bad lighting conditions.

The centroid method for the correction of turbulence consists in computing the Karcher-Fréchet mean of the sequence of input images. The direction of deformation between a pair of images is determined by the optical flow. A distinguishing feature of the centroid method is that it can produce useful results from an arbitrarily small set of input images.