Single-Core Multiscale Residual Network for the Super Resolution of Liquid Metal Specimen Images

Keqing Ning,Siyu Han,Kai Han,Zhihao Zhang,Xiqing Zhang

doi:10.3390/make3020023

Abstract

In a gravity-free or microgravity environment, liquid metals without crystalline nuclei achieve a deep undercooling state. The resulting melts exhibit unique properties, and the research of this phenomenon is critical for exploring new metastable materials. Owing to the rapid crystallization rates of deeply undercooled liquid metal droplets, as well as cost concerns, experimental systems meant for the study of liquid metal specimens usually use low-resolution, high-framerate, high-speed cameras, which result in low-resolution photographs. To facilitate subsequent studies by material scientists, it is necessary to use super-resolution techniques to increase the resolution of these photographs. However, existing super-resolution algorithms cannot quickly and accurately restore the details contained in images of deeply undercooled liquid metal specimens. To address this problem, we propose the single-core multiscale residual network (SCMSRN) algorithm for photographic images of liquid metal specimens. In this model, multiple cascaded filters are used to obtain feature information, and the multiscale features are then fused by a residual network. Compared to existing state-of-the-art artificial neural network super-resolution algorithms, such as SRCNN, VDSR and MSRN, our model was able to achieve higher PSNR and SSIM scores and reduce network size and training time.

Highlights

Deep undercooling is a type of rapid solidification technique for preparing novel materials
To obtain accurate state information about these deeply undercooled liquid metal droplets, the low-resolution photographs are reconstructed by super resolution—this is currently the most widely used approach to study the properties of liquid metal specimens
This work proposes an image super-resolution network model for photographic images of liquid metal specimens, which uses factorized convolution to reduce the tremendous number of model parameters that result from the use of large convolutional cores for multiscale feature extraction and, improves training efficiency for network models of this type

Summary

Introduction

Deep undercooling is a type of rapid solidification technique for preparing novel materials. Compared to the rapid quenching technique, deep undercooling allows alloys to rapidly solidify with slow cooling. This process provides a new means of studying some of the nonequilibrium phenomena that occur during rapid alloy solidification, and it allows for the preparation of new materials with various outstanding properties, which are otherwise impossible to obtain by conventional solidification techniques [1]. Laser heaters are used to heat the levitated material, while cameras are used to record this process and photograph the deeply undercooled liquid metal droplet, which is levitated by the vacuum levitation apparatus after it is melted by laser heaters. To obtain accurate state information about these deeply undercooled liquid metal droplets, the low-resolution photographs are reconstructed by super resolution—this is currently the most widely used approach to study the properties of liquid metal specimens

Methods

Results

Conclusion