A Novel DnCNN-Based Approach for Efficient Video Stabilization and Noise Reduction

The video stabilization technique which operates before video processing requires effective noise removal procedures for maximize motion estimation results along with visual quality improvement. Traditional denoising protocol like Gaussian filtering and median filtering and bilateral transforms handle general noise reduction in various scenarios. These methods tend to face a trade-off between successful noise elimination and protection of crucial structural details so they cause image blurring throughout the process. Through its customized denoising convolutional neural networks (DnCNN) approach the proposed method proves to have better results than traditional methods by learning complex noise patterns and maintaining spatiotemporal consistency. This paper examines the differences between standard and deep learningbased denoising methods when stabilizing videos. To assess their performance, we use the PSNR, SSIM and MAE objective evaluation metrics.