Research on Multiscale Detail Enhancement in Graphic Design Images Based on Visual Perception

With the rapid evolution of digital technology, graphic design has become increasingly pivotal across various domains. While traditional image enhancement methods have addressed issues in texture boundaries and information retrieval, they often neglect challenges posed by noise in graphic design, leading to uneven enhancements. Therefore, this study proposes a multi-scale detail enhancement method to improve the visual perception quality of graphic design images. Nonlinear transformation is applied to the image to obtain a preliminary enhanced image. Subsequently, both the preliminary enhanced image and the low brightness image are simultaneously fed into a multi-scale feature extraction block for feature extraction. In order to improve the ability of online learning of semantic features, a U-shaped feature enhancement module is introduced in each scale feature extraction branch, which increases the feature extraction of contextual information. Finally, the enhanced image is obtained by integrating multi-scale feature information. The experimental results show that this method is relatively superior in terms of visual effects and metrics, and significantly improves color restoration, texture preservation, and detail enhancement, providing a promising direction for image enhancement in graphic design.