Wavelet Transform Techniques for Image Enhancement

Wavelet transform-based image enhancement serves as a widely adopted methodology in digital image processing. The technique initiates with a non-subsampled wavelet decomposition of the original image to extract wavelet coefficients. Subsequently, high-frequency decomposition coefficients at each decomposition level undergo specialized processing operations—commonly involving thresholding, coefficient modification, or amplification—to generate enhanced wavelet coefficients. The final stage involves wavelet reconstruction to synthesize the enhanced image. From an implementation perspective, this process typically utilizes wavelet functions (e.g., Daubechies or Symlets) through Python's PyWavelets library or MATLAB's wavelet toolbox, where key functions include wavedec2 for decomposition and waverec2 for reconstruction. Though computationally intensive due to multi-scale coefficient processing, this method proves particularly effective for noise reduction, edge sharpening, and contrast enhancement in various imaging applications.