Image Completion with Hybrid Interpolation in Tensor Representation

Interpolation using Hankel tensor completion

We present a novel multidimensional seismic trace interpolator that works on constant-frequency slices. It performs completion on Hankel tensors whose order is twice the number of spatial dimensions. Completion is done by fitting a PARAFAC model using an Alternating Least Squares algorithm. The new interpolator runs quickly and can better handle large gaps and high sparsity than existing comple...

Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

Hybrid Singular Value Thresholding for Tensor Completion

In this paper, we study the low-rank tensor completion problem, where a high-order tensor with missing entries is given and the goal is to complete the tensor. We propose to minimize a new convex objective function, based on log sum of exponentials of nuclear norms, that promotes the low-rankness of unfolding matrices of the completed tensor. We show for the first time that the proximal operato...

3d Interpolation Using Hankel Tensor Completion by Orthogonal Matching Pursuit

Introduction. Seismic data are often sparsely or irregularly sampled along one or more spatial axes. Irregular sampling can produce artifacts in seismic imaging results, thus multidimensional interpolation of seismic data is often a key processing step in exploration seismology. Many solution methods have appeared in the literature: for instance in Spitz (1991), Sacchi (2000) it was proposed to...

Off the grid tensor completion for seismic data interpolation