Advanced Video Preprocessing with HAMDNet: A Denoising Approach for Stability and Quality

In hand-held and drone-based video capture, video stabilization is significant for improving visual quality and user experience. Nevertheless, the performance of stabilizing methods is vulnerable to noisy frames. To develop frame preprocessing, we proposed a Hybrid Adaptive Multi-Scale Denoising Network (HAMDNet), which combines motion-aware filtering, deep learning via noise estimation, and multi-scale feature extraction. The suggested method produces better stabilization results by greatly improving frame quality while maintaining temporal consistency. HAMDNet is the best option for video stabilization applications since experimental assessments show that it outperforms traditional denoising approaches in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).