Image Edge Enhancement Implementation

The program begins by employing wavelet transform for image analysis and processing. Wavelet transform serves as a mathematical tool that decomposes signals or images into frequency components at different scales, typically implemented using functions like wavedec2() in MATLAB for 2D discrete wavelet decomposition. Through this transformation, we extract detailed information about the image and effectively identify edge features within the image structure. Subsequently, the program performs noise filtering operations using thresholding techniques such as soft or hard thresholding applied to wavelet coefficients, which involves functions like wthresh() for coefficient modification. This filtering process significantly improves the quality and clarity of edge effects by eliminating high-frequency noise components while preserving important edge information. Through these systematic processing stages - involving wavelet decomposition, coefficient thresholding, and inverse wavelet reconstruction using waverec2() - we obtain an image with enhanced edge effects that demonstrates greater visual appeal and informational richness for subsequent computer vision applications.