Wavelet Image Denoising Algorithm Using Bayesian (BAYES) Thresholding

This algorithm implements image denoising through Bayesian thresholding of wavelet coefficients. The core methodology involves applying Bayesian statistical thresholds to determine which wavelet coefficients should be preserved and which should be discarded to effectively reduce noise in images. The implementation typically follows these steps: wavelet decomposition of the noisy image, statistical analysis of coefficients using Bayesian priors, threshold application based on Bayesian risk minimization, and wavelet reconstruction of the denoised image. Key implementation aspects include: - Wavelet transform application (using functions like wavedec2 in MATLAB for 2D images) - Bayesian threshold calculation that adapts to local signal characteristics - Coefficient shrinkage or hard thresholding based on Bayesian decision theory - Parameter tuning for threshold values to control the denoising intensity The algorithm allows precise control over denoising strength by adjusting threshold parameters, balancing noise removal with detail preservation. This approach is widely employed in image processing applications and has demonstrated excellent performance across various practical scenarios, particularly in medical imaging, photography enhancement, and scientific image analysis where statistical noise models are appropriate.