Image Denoising Using Discrete Wavelet Transform (DWT)

In the field of digital signal processing, wavelet transform finds extensive applications in image denoising. Discrete Wavelet Transform (DWT) is a mathematical operation that decomposes signals into sub-bands of different frequencies, enabling superior signal analysis and processing. For image denoising applications, DWT employs multi-level decomposition and reconstruction techniques to effectively remove noise and other artifacts from images. The implementation typically involves applying DWT filters (like Haar, Daubechies, or Symlets) to separate image components into approximation and detail coefficients. Thresholding algorithms (hard or soft thresholding) are then applied to the detail coefficients to suppress noise components before reconstructing the enhanced image through inverse DWT. This makes DWT an essential tool for improving image quality and reducing noise interference in digital image processing workflows. The process can be implemented using MATLAB's wavelet toolbox functions such as wavedec2 (for 2D decomposition), wthresh (for thresholding), and waverec2 (for reconstruction), or through Python libraries like PyWavelets with similar functionality.