Wavelet Transform-Based Image Enhancement

Wavelet transform-based image enhancement is an image processing technique that improves image quality and visual effects through the application of wavelet transforms. The complete workflow involves multiple stages including preprocessing, wavelet decomposition, enhancement processing, and postprocessing. During preprocessing, images undergo preparation steps such as noise removal using Gaussian filtering and smoothing operations. Subsequently, wavelet transform is applied to decompose the image into frequency and spatial information components using functions like wavedec2() in MATLAB. The enhancement phase involves modifying wavelet coefficients to improve contrast through histogram equalization, adjust brightness via gamma correction, and enhance details using coefficient thresholding. Finally, postprocessing performs image reconstruction through inverse wavelet transform (waverec2()) followed by optimization techniques like edge sharpening. This methodology provides an effective approach to enhance image quality, resulting in clearer and more vivid visual representations. Typical implementation involves algorithms like soft/hard thresholding for denoising and multiscale analysis for detail enhancement across different wavelet decomposition levels.