Image upsampling and noise removal are important tasks in digital image processing. Single-image upsampling and denoising influence the quality of the resulting images. Image upsampling is known as super-resolution (SR) and referred to as the restoration of a higher-resolution image from a given low-resolution image. In facial expression analysis, the resolution of the original image directly affects the reliability and validity of the emotional analysis. Hence, optimization of the resolution of the extracted original image during emotion analysis is important. In this study, a model is proposed, which applies an image super-resolution method to an algorithm that classifies emotions from facial expressions.

中文翻译：

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使用改进的基于深度学习的面部表情分析的图像超分辨率模型

图像上采样和噪声去除是数字图像处理中的重要任务。单图像上采样和去噪会影响生成的图像的质量。图像上采样被称为超分辨率 (SR)，称为从给定的低分辨率图像恢复更高分辨率的图像。在面部表情分析中，原始图像的分辨率直接影响情感分析的信度和效度。因此，在情感分析期间优化提取的原始图像的分辨率很重要。在这项研究中，提出了一种模型，该模型将图像超分辨率方法应用于从面部表情中分类情绪的算法。