Application of image sharpening based quality assessment model : Extraction of Traditional Chinese Embroidery Patterns as an Example

Today's computer software has made many impressive advances in the field of digital image processing of tattoos. The digital image processing of traditional patterns can not only help design researchers better understand and analyze the structure and evolution of traditional patterns, but also provide new ideas and methods for the application and innovation of patterns. Through computer software, we can perform accurate edge detection, texture extraction, color analysis and morphological reconstruction of traditional patterns, making the characteristics and beauty of traditional patterns better displayed and conveyed. However, traditional image quality assessment methods often fail to adequately reflect the complexity of visual perception; therefore, this study proposes a deep learning-based assessment model to more accurately assess the effect of image sharpening. By building a dataset covering a wide range of image types, including traditional Unsharp Masking and high-pass filter sharpening as well as deep learning-based methods, we simulate real-world application scenarios. Using a Convolutional Neural Network (CNN) structure and incorporating an attention mechanism, the model focuses on the detail changes in the sharpened region of the image. The experimental results demonstrate the superior performance of our model by comparing traditional PSNR, SSIM and deep learning evaluation methods. Our study provides an effective tool for the field of image processing and shows the direction for future research on image quality assessment.