Image Denoising Using Wavelet Transform

Wavelet transform-based image denoising is a widely adopted method in signal processing. This approach allows for noise reduction effectiveness adjustment through the selection of different soft and hard thresholding functions. When choosing appropriate thresholding functions, developers should consider both the noise characteristics of the image and the specific noise types targeted for removal. In practical implementation, threshold parameters can be optimized using functions like wthrmngr in MATLAB or similar threshold management utilities in Python's PyWavelets library. By fine-tuning these threshold parameters through iterative optimization algorithms, we can precisely control the denoising intensity, thereby producing cleaner and more accurate image results. The wavelet decomposition process typically involves functions such as wavedec2 for 2D signals, while threshold application utilizes pointwise operations on detail coefficients. This method's flexibility stems from the multiresolution analysis capability of wavelets, making it particularly effective for preserving image edges while removing noise contaminants.