MSV-Net: Visual Super-Resolution Reconstruction for Scientific Simulated Data of Mixed Surface-Volume

Abstract

High fidelity visual analysis usually relies on high-resolution grid data of coupled geometric models generated by large-scale scientific simulation, which brings great challenges to data storage and smooth interaction. Therefore, this paper proposes a super-resolution reconstruction method for large-scale scientific simulated data of mixed surface-volume, which is MSV-Net. The network is an end-to-end deep neural network, which realizes the joint learning of hybrid rendering mapping from low resolution data to high resolution data through multi-layer nonlinear transformation. The network without the fully connected layer, can not only reduce the network parameters, but also improve the flexibility and reusability of the network. In addition, MSV-Dataset, a surface-volume mixed dataset for large-scale electromagnetic simulation application is constructed for model training and verification. This dataset consists of mixed rendered images using nontransparent geometric model rendering coupled with semitransparent volume rendering. The proposed method is compared with a variety of traditional and deep learning methods. The quantitative analysis results show that the MOS absolute evaluation index of this method reaches 4.1, and the reconstruction accuracy is second only to the real image; it takes 66.28 s to directly draw mixed data with 1500×1500 image resolution, but only 4.14 s with the proposed method. The interaction performance is improved about 15 times.

Keywords

• scientific data visualization; hybrid rendering; large-scale simulation; super-resolution reconstruction;  deep learning