Advanced Model for Oracle Bone Image Denoising and Feature Extraction Based on Multi-Algorithm Integration and Wavelet Transform

As an important part of ancient Chinese cultural heritage, oracle bone image has important historical and cultural value. However, due to the unique image features and noise interference, effective image processing and analysis are still facing great challenges. In this paper, we propose an advanced model for oracle bone image denoising and feature extraction based on multi-algorithm ensemble and wavelet transform. Firstly, wavelet transform is used for image denoising to effectively remove noise and retain key image features. Then, the image contrast is enhanced by gray inversion and binarization, and the image structure is simplified by skeletonization, which lays the foundation for subsequent feature extraction. In the frequency domain analysis, Fourier transform is applied to reveal the spectral characteristics of the image, so as to understand and analyze the image data more deeply. In order to accurately determine the edges and corners of oracle images, this study compared several edge detection algorithms (such as Canny, Sobel, and Prewitt) and corner detection algorithms (such as Harris, SURF, and ORB). The results show that Canny performs well in edge detection and noise suppression, while SURF and ORB are more suitable for real-time processing and resource constrained environments. This model provides a new method for the digital preservation and dissemination of oracle bone images, and establishes a benchmark for the optimization design of algorithms in the field of image analysis.