Image super-resolution model using an improved deep learning-based facial expression analysis

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.

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