Bayesian Threshold Method for Image Denoising Implementation

In this paper, we employed the Bayesian threshold method for image denoising. This approach builds upon the simulation results from the research paper "Chang: Adaptive Wavelet Thresholding for Image Denoising and Compression." The implementation typically involves wavelet decomposition of the image, followed by applying Bayesian-derived thresholds to the wavelet coefficients. Key functions include wavelet transform computation (using functions like wavedec2 in MATLAB), threshold calculation based on Bayesian statistics, and inverse wavelet transformation for image reconstruction. We observed that the Bayesian threshold method demonstrates excellent performance in image denoising applications. By implementing threshold processing on wavelet coefficients, we effectively reduce noise while significantly enhancing image clarity and quality. The algorithm automatically adapts thresholds based on the statistical properties of wavelet coefficients at different scales, making it particularly effective for preserving image edges and details. Therefore, we conclude that the Bayesian threshold method represents an effective image denoising technique worthy of further research and exploration in practical applications. Code implementation typically involves parameter optimization for different noise levels and image characteristics to achieve optimal denoising results.