Image Smoothing Based on Wavelet Transform

This program implements an image smoothing method based on wavelet transform that effectively processes images to enhance detail clarity. The algorithm performs wavelet decomposition to separate high-frequency and low-frequency components of the image. The core implementation involves applying smoothing filters specifically to the low-frequency subbands while preserving high-frequency details. After processing the low-frequency components, the algorithm reconstructs the image by combining the smoothed low-frequency parts with the original high-frequency components. This approach significantly reduces image noise and discontinuities while maintaining key features, resulting in smoother and more natural-looking images. The implementation typically utilizes wavelet functions like Daubechies or Haar wavelets through functions such as wavedec2() and waverec2() for decomposition and reconstruction. The program offers flexibility through adjustable parameters including wavelet type selection, decomposition level, and thresholding techniques, allowing users to optimize smoothing effects for different requirements. This makes it a practical image processing tool widely applicable in digital image processing, computer vision, and related fields where noise reduction and feature preservation are crucial.