Classical Image Smoothing and Denoising Algorithm Implementations

The following section presents implementations of several classical image smoothing and denoising algorithms for your reference. The collection notably includes wavelet-based denoising approaches using both soft-thresholding (which provides smoother transitions by shrinking coefficients towards zero) and hard-thresholding (which completely removes coefficients below a threshold while preserving others). These wavelet implementations typically involve decomposition using filters like Daubechies, thresholding operations on detail coefficients, and reconstruction. Furthermore, three image quality assessment programs are included to quantitatively evaluate denoising results using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), which compare processed images against clean reference images. We hope these additional resources prove valuable for your image processing projects and algorithm evaluation needs.