Wavelet Transform-Based Image Denoising Processing

This program performs image denoising by applying wavelet transform algorithms to enhance image quality and clarity. Through selection of appropriate wavelet basis functions (such as Haar, Daubechies, or Symlets) and thresholding strategies (including hard/soft thresholding methods), it effectively removes various types of image noise while preserving essential features and fine details. The implementation typically involves multilevel wavelet decomposition, threshold application to detail coefficients, and wavelet reconstruction. The program provides multiple adjustable parameters including wavelet type selection, decomposition level control, and threshold value customization, allowing users to perform personalized denoising according to specific requirements. By utilizing this tool, users can easily improve visual quality and obtain clearer, more authentic image representations. Therefore, this program serves as a practical and convenient solution suitable for diverse image denoising applications in fields like medical imaging, photography, and computer vision.