Bayesian (BAYES) Threshold-Based Wavelet (WAVELET) Image Denoising Algorithm Source Code

In this document, I present a wavelet-based image denoising algorithm that utilizes Bayesian thresholding. The primary objective of this algorithm is to reduce image noise by applying Bayesian threshold processing to wavelet coefficients. Through this method, we can effectively eliminate noise from images, thereby enhancing image quality and clarity.

The implementation typically involves several key steps: First, the noisy image is decomposed using wavelet transform (such as DWT) to obtain wavelet coefficients at different scales. Then, Bayesian threshold values are calculated for each subband based on statistical properties of the coefficients. The algorithm applies soft or hard thresholding to suppress noise-dominated coefficients while preserving important image features. Finally, the inverse wavelet transform reconstructs the denoised image.

Key functions in the source code may include wavelet decomposition routines, Bayesian threshold calculation functions, coefficient thresholding operations, and image reconstruction modules. The Bayesian approach adapts thresholds according to local statistical characteristics, making it particularly effective for handling various noise types and preserving edge information.