Innovative adaptive edge detection for noisy images using wavelet and Gaussian method

Edge detection is a crucial task in image processing and remote sensing, particularly for accurately identifying and separating shapes in noisy digital images. To enhance robustness and detail in edge detection, this study presents an innovative edge detection method, which integrates a denoising module and an adaptive thresholding technique to effectively address challenges associated with Gaussian noise in images. The proposed denoising module employs wavelet and Gaussian denoising functions to decompose, filter, and reconstruct the image, thereby reducing the impact of noise and enhancing image quality. For edge detection, an adaptive thresholding method based on a modified OTSU method is utilized. Comprehensive experiments validate the proposed framework by comparing detected edges against ground truth across various levels of Gaussian noise (0.1%, 10%, 20%, and 30%). The median thresholding function is chosen for its stability and convenience, while hard thresholding is avoided due to its tendency to introduce artifacts. Objective metrics, including Mean Squared Error (MSE), Accuracy, and Peak Signal-to-Noise Ratio (PSNR), are employed for evaluation. Comparative results indicate that the proposed method outperforms traditional methods, such as Canny and Roberts, showcasing its effectiveness in edge detection.