Stationary Wavelet Transform with Image Denoising Applications and Quality Metrics

Using Stationary Wavelet Transform (SWT) for image denoising is a widely adopted method in digital image processing. SWT enables multi-scale analysis by decomposing images into different frequency bands without decimation, effectively preserving texture details while reducing noise. In implementation, SWT typically involves applying wavelet filters recursively across multiple scales using functions like swt2() in MATLAB, which maintains translation invariance by avoiding downsampling operations. For quality assessment, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) serve as standard metrics to evaluate denoising performance. PSNR calculates reconstruction quality by measuring the mean squared error between original and denoised images, where higher values indicate better noise suppression. SSIM provides a more comprehensive evaluation by considering luminance, contrast, and structural similarities through local window-based comparisons. Code implementation typically involves thresholding wavelet coefficients at different decomposition levels using soft or hard thresholding functions, followed by inverse SWT reconstruction. The combination of SWT-based denoising with PSNR/SSIM validation offers an effective framework for optimizing noise reduction parameters and comparing algorithm performance across various image types and noise distributions.