We present a novel neural representation for light field content that enables compact storage
and easy local reconstruction with high fidelity. We use a fully-connected neural network to …

Recently, large scale seismic data acquisition has been a critical method for scientific
research and industrial production. However, due to the bottleneck on data transmission and …

We introduce a framework for compactly representing light field content with the novel
concept of neural subspaces. While the recently proposed neural light field representation …

The next generation of oil and gas exploration technology is moving toward large-scale
seismic acquisition, automation, and flexibility. This phenomenon has accelerated the …

Seismic survey is one of the most effective tools for oil and gas exploration. To date, there
has been an exponential growth in the size of seismic data required for large-scale seismic …

Data transmission is crucial in the process of equipment monitoring. The compression
algorithms are adopted to reduce the amount of data transmission. When sparse encoding …

Shadow artifacts caused by high scattering of the superficial vessels will cause structural
and vascular discontinuities in optical coherence tomography (OCT) images. Large blood …

The ongoing advances in computational photography have introduced a range of new
imaging techniques for capturing multidimensional visual data such as light fields, BRDFs …

In this paper, we propose a new sparse dictionary learning scheme for lossy compression of
seismic signals collected at a single sensor from multiple source shots. The method …

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is
expected that oil & gas will remain a substantial sources of energy in a coming years. To find …