The containerless method is generally used to study the intrinsic properties of materials, especially the thermophysical properties of melt droplets. The calculation of the melt droplet density and thermal expansion coefficient is related to its volume, while density is the dependent variable for determining the surface tension and viscosity coefficient. Evidently, the accuracy of the thermophysical properties of materials essentially depend on the precision of volume measurement. The melt droplet volume is obtained by analysing the image, thus, the precise volume of the melt droplet depends on the image quality and contour extraction algorithm. Restricted by external conditions, most of the obtained melt droplet images are of low quality and are severely polluted by noise, which complicates the determination of the thermophysical characteristics. Herein, a shape-supervised super-resolution convolutional neural network method is presented to improve image resolution and using its sub-network to extract the contour of the melt droplet directly and accurately. Compared with the existing method, this approach improves the accuracy of evaluating the thermophysical properties of the material and reduces the computational complexity by simplifying the two-step calculation process to a one-step procedure.

无容器法一般用于研究材料的内在性质，尤其是熔滴的热物理性质。熔滴密度和热膨胀系数的计算与其体积有关，而密度是确定表面张力和粘度系数的因变量。显然，材料热物理性质的准确性主要取决于体积测量的精度。熔滴体积是通过分析图像获得的，因此熔滴的精确体积取决于图像质量和轮廓提取算法。受外部条件的限制，大多数获得的熔滴图像质量低，噪声污染严重，给热物理特性的测定带来了复杂的问题。在此，提出了一种形状监督的超分辨率卷积神经网络方法来提高图像分辨率，并利用其子网络直接准确地提取熔滴的轮廓。与现有方法相比，该方法通过将两步计算过程简化为一步过程，提高了材料热物理性能评估的准确性，降低了计算复杂度。