Image Denoising Using Wavelet Transform

Wavelet transform-based image denoising represents a widely adopted method in digital image processing. This technique effectively reduces noise and interference in images while enhancing overall clarity and quality. Implementation typically begins with preprocessing steps including grayscale conversion and preliminary noise reduction. The core algorithm involves selecting appropriate wavelet basis functions (such as Haar, Daubechies, or Symlets) and applying thresholding techniques to wavelet coefficients. Hard thresholding completely eliminates coefficients below a certain value, while soft thresholding shrinks coefficients toward zero. Key implementation steps include: 1. Performing multi-level wavelet decomposition using functions like wavedec2() in MATLAB 2. Calculating optimal thresholds using methods like VisuShrink or SureShrink 3. Applying inverse wavelet transform to reconstruct the denoised image This methodology proves effective for both static image processing and video sequence denoising when applied frame-by-frame. The wavelet transform's multi-resolution analysis capability enables selective noise removal across different frequency bands while preserving important image features. The implementation provides configurable parameters for wavelet type, decomposition level, and threshold selection to accommodate various noise characteristics. This powerful approach significantly enhances image quality through intelligent frequency-domain processing. Please feel free to contact me for any technical discussions or implementation queries regarding this wavelet-based denoising solution.