Image Denoising Using Wavelet Transform

This program implements image denoising using wavelet transform methodology, achieving superior noise reduction results through multi-scale analysis. The algorithm performs wavelet decomposition of the noisy image using functions like wavedec2, applies thresholding techniques (soft/hard thresholding) to wavelet coefficients at different scales, and reconstructs the denoised image using waverec2. Users can optimize denoising performance by adjusting key parameters including wavelet basis functions (Daubechies, Symlets, etc.), decomposition scales, and threshold values through systematic parameter tuning. The implementation features an intuitive GUI interface built using MATLAB's App Designer or GUIDE, providing interactive controls for parameter adjustment and real-time visualization of denoising results. The interface includes components for wavelet selection, threshold specification, and side-by-side comparison of original versus denoised images. The code structure follows modular design principles with separate functions for: - Wavelet decomposition and coefficient extraction - Threshold calculation and application - Image reconstruction and quality assessment - PSNR and SSIM metric computation for objective evaluation Both beginners and experienced professionals can efficiently utilize this program for image denoising tasks, obtaining satisfactory results across various noise types (Gaussian, salt-and-pepper, etc.). The implementation includes comprehensive comments and example usage scenarios to facilitate understanding of wavelet-based denoising principles.