Rearranging Multidimensional Graphic Elements for Graphic Design Applications of Image Enhancement
Zhongda Cao · International Journal of Pattern Recognition and Artificial Intelligence · 2025
This study looks into rearranging multidimensional visual elements in graphic design for picture enhancement. It also suggests a multidimensional method for enhancing histograms and combining graphic elements that are based on the framework of a visual communication system. The three primary components of the system are an output and visual graphic display module, a data processing module, and an image collector. First, the Scale-Invariant Feature Transform (SIFT) feature description method is used to extract the multidimensional features from the graphical elements, and the feature transformation matrix calculation is used to achieve feature alignment. Subsequently, employing the window-based graphic element fusion technique, the “energy” metric determines the ideal pixel value to guarantee the correctness and smoothness of the fusion outcome. The graphic design image’s histogram is then created, denoised, and enhanced utilizing a combination of standard variance and entropy as the local complexity measurement factor. In the end, MATLAB 2020a simulation tests are conducted, and peak signal-to-noise ratio and structure similarity index are used to confirm the effectiveness of this paper’s approach. The experimental findings demonstrate that the suggested method effectively denoises and enhances photos and can greatly increase images’ visual impact and detail richness. This work offers theoretical justification and a novel, efficient technique for optimizing graphic design pictures.