MATLAB Implementation of Wavelet Threshold Denoising

Below is the source code for wavelet threshold denoising, where you can modify threshold functions based on specific requirements to achieve different functionality. This algorithm effectively removes noise from images or signals, significantly improving data quality and clarity. The implementation follows these key computational steps:

1. Perform wavelet transform on the input image or signal to obtain wavelet coefficients - typically using functions like wavedec() for decomposition.

2. Apply threshold processing to wavelet coefficients according to the selected threshold function - common implementations include hard thresholding (wthresh(coefs,'h',threshold)) and soft thresholding with customized threshold calculation methods.

3. Reconstruct the denoised image or signal using inverse wavelet transform (waverec()) based on the threshold-processed coefficients.

You can freely modify threshold functions such as universal threshold, minimax threshold, or SURE threshold to optimize denoising performance for specific applications. The core algorithm involves coefficient thresholding where small coefficients (likely noise) are suppressed while preserving significant signal components. Hope this information proves helpful for your implementation!