Multi-task super resolution method for vector field critical points enhancement

Yilun Yang, Zhou Hao, Weilong Peng, Keke Tang, Meie Fang

Article ID: 2103
Vol 3, Issue 1, 2022
DOI: https://doi.org/10.54517/met.v3i1.2103
VIEWS - 304 (Abstract)

Abstract

It is a challenging task to handle the vector field visualization at local critical points. Generally, topological based methods firstly divide critical regions into different categories, and then process the different types of critical regions to improve the effect, which pipeline is complex. In the paper, a learning based multi-task super resolution (SR) method is proposed to improve the refinement of vector field, and enhance the visualization effect, especially at the critical region. In detail, the multi-task model consists of two important designs on task branches: one task is to simulate the interpolation of discrete vector fields based on an improved super-resolution network; and the other is a classification task to identify the types of critical vector fields. It is an efficient end-to-end architecture for both training and inferencing stages, which simplifies the pipeline of critical vector field visualization and improves the visualization effect. In experiment, we compare our method with both traditional interpolation and pure SR network on both simulation data and real data, and the reported results indicate our method lower the error and improve PSNR significantly.


Keywords

critical point; vector field visualization; multiple tasks; super resolution

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