Artistic Style Analysis of Root Carving Visual Image Based on Texture Synthesis

Abstract

Texture synthesis is a hotspot of research in the field of realism and nonphotorealistic rendering, with applications in virtual reality, computer vision, and other areas. It has a wide application prospect in image editing, filling of defective images, data compression, rapid transmission of network data, generation of large-scale scenes, and rendering of realistic and nonrealistic images, and it is used to solve the problems of seam, distortion, and parameter adjustment of traditional methods. Root carving is a one-of-a-kind plastic art form that dates back to the Xia and Shang dynasties and peaked during the Ming and Qing dynasties. Texture synthesis uses existing texture data to create a large-area texture that looks similar. This technology detects matching points in the sample texture and copies them to the output image, resulting in a texture image that is similar to and continuous with the sample texture. Realistic and nonrealistic rendering, image restoration, and computer-aided design are just a few of the applications. Texture can describe a variety of natural phenomena with repeating features, as well as expressing the rich details of an object’s surface. Because of its characteristics of both practicality and artistry, as well as the coexistence of popularity and elegance, the art of root carving retains its unique charm despite its ups and downs as a more than 2,000-year-old art form. A genetic evolution search strategy is used to propose an optimization search algorithm in this paper. We further investigate the ecological aesthetic characteristics of root carving art, in accordance with the nature of its aesthetics. By using a genetic search strategy, you will be able to find what you are looking for faster. A better matching block can be quickly found to complete the search for the best matching block, speeding up the algorithm’s search process and ensuring better synthesis results.

Animation Vr Scene Stitching Modeling Based on Genetic Algorithm

“…Self-organization, adaptability, and the ability to learn are all features. It is not constrained by the specifics of the issue at hand, and it may deal with complicated situations that are challenging for conventional optimization procedures.In research [16], an optimization search method is proposed using a genetic evolution search technique. In keeping with the artistic aspect of root-cutting art, they further explore its ecological aesthetic features.…”

“…The experimental findings presented in the study demonstrate that the K-DOPs method has a frame rate of 6.7. [16] A genetic evolution search approach is used to suggest an optimization search method. They go on to examine the ecologically aesthetic aspects of root-cutting art, continuing with its artistic character.…”