Image Edge Detection Using Directionally Adjustable Wavelet Transform

By employing directionally adjustable wavelet transforms, more precise image edge detection can be achieved. The wavelet transform's directional adaptability allows for optimal orientation selection to capture edge features at various angles. The experimental process includes comprehensive analysis of wavelet filter design and parameter optimization for edge detection. Our implementation involves creating custom wavelet filters with adjustable orientation parameters using MATLAB's wavelet toolbox functions like modwt and dwt2. The detailed experimental report presents comparative results between traditional edge detection methods (Sobel, Canny) and our directional wavelet approach, demonstrating superior edge continuity and noise immunity. The report includes visual comparisons of original images alongside processed results, with quantitative metrics (PSNR, SSIM) for objective evaluation. Key implementation aspects cover wavelet coefficient thresholding techniques and multi-scale edge fusion algorithms for enhanced detection accuracy.