Abstract: To address the current issues of low accuracy and stability in contact angle measurement,this paper proposes a contact angle measurement method based on an improved lightweight enhanced super-resolution network LESRCNN(lightweight enhanced  super-resolution CNN).The improved network replaces the original information extraction module with a feature extractor composed of ConvNeXt Blocks,which are enhanced by borrowing the Swin-T structure from ResNet50,to boost super-resolution performance.Additionally,an enhanced spatial attention (ESA) mechanism is introduced to improve the network's ability to capture fine-grained image details,and the Gaussian error linear unit (GELU) is adopted instead of the rectified linear unit (ReLU) as the activation function to accelerate convergence.Furthermore,to enhance the robustness of the fitting results,this paper employs the Huber function as the weighting function and utilizes an ellipse fitting method based on iteratively reweighted least squares (IRLS) to simultaneously fit the droplet contours from both left and right sides for contact angle calculation.Experimental results demonstrate that,at a scaling factor of 3,the improved network achieves a peak signal-to-noise ratio (PSNR) increase of 0.8 dB and a structural similarity index (SSIM) improvement of 0.0026 compared to the original network when trained on the same droplet dataset.For standard samples with contact angles below 90°,the accuracy and stability of measurements improved by 34.3% and 7.4%,respectively,while for samples with angles of 90° or above,the improvements were 18.2% and 29.4%,respectively.

Key words: contact angle measurement, super-resolution reconstruction, droplet contour fitting