The proposed CGDenoiser can be extended to address other types of image degradations, such as motion blur and compression artifacts, by incorporating additional modules that specifically target these issues. One approach could involve integrating a motion estimation algorithm that detects and compensates for motion blur before the denoising process. This could be achieved by utilizing optical flow techniques or deep learning-based motion estimation networks to predict the motion vectors, allowing the system to apply deblurring techniques tailored to the detected motion.
For compression artifacts, the CGDenoiser could be enhanced by training a separate generative model that focuses on reconstructing high-quality images from compressed inputs. This model could leverage adversarial training to learn the distribution of clean images from their compressed counterparts, effectively reducing blockiness and ringing artifacts. By combining these specialized modules with the existing architecture of CGDenoiser, the system could become a comprehensive solution for various image degradations, thereby improving UAV tracking performance in a wider range of low-light conditions.