Automated Weather-Degraded Image Restoration Using Curvelet Transform Edge Detection

This approach utilizes a curvelet transform edge detection algorithm to automatically restore images degraded by weather conditions. The algorithm employs multi-scale and multi-directional analysis through curvelet coefficients to precisely detect weather-induced artifacts such as blurring, distortion, and noise. Key implementation steps include: 1) Applying discrete curvelet transform via FDCT_USFFT (Fast Discrete Curvelet Transform using Unequally-Spaced Fast Fourier Transform) to decompose the image into different scales and angles, 2) Implementing adaptive thresholding on curvelet coefficients to distinguish edges from noise, 3) Using morphological operations to refine edge maps, and 4) Applying inverse curvelet transform with modified coefficients to reconstruct enhanced images. This method effectively restores images to their original clarity and authenticity by preserving critical edge information while suppressing weather-related distortions. The algorithm significantly improves image quality by enhancing detail visibility and optimizing visual experience. Furthermore, it finds broad applications in weather forecasting, autonomous driving, and security surveillance systems, providing reliable image processing and analytical capabilities through robust edge preservation and noise suppression mechanisms.