Fractional B-spline Wavelets for Image Denoising

This document presents a MATLAB program that utilizes fractional B-spline wavelets for image denoising. Image denoising represents a crucial technique in digital image processing, aimed at reducing noise contamination while enhancing overall image quality. The fractional B-spline wavelet approach offers significant advantages, including superior preservation of image detail characteristics and effective noise suppression capabilities. The program implements key algorithmic components including: - Fractional B-spline wavelet transformation using customized filter banks - Multi-scale thresholding strategies for noise reduction - Inverse wavelet reconstruction with optimized parameter settings The MATLAB code structure features: 1. Main denoising function with configurable parameters (wavelet order, threshold type, decomposition levels) 2. Wavelet coefficient processing module implementing soft/hard thresholding techniques 3. Image quality evaluation metrics (PSNR, SSIM) for performance validation Through this implementation, you will gain comprehensive understanding of fractional B-spline wavelet applications in image denoising. The modular code design allows straightforward customization and optimization based on specific requirements. The program serves as both an educational resource and practical tool for researchers and engineers working in image processing domains.