Enhancing Video Clarity with AI: A Low-Rank Tensor Recovery and Proximal Support Vector Machine Algorithm Approach

This study aims to enhance video clarity using AI-driven low-rank tensor recovery and Proximal Support Vector Machine (PSVM) algorithms. Noise and blur in video processing have always been challenging issues, and traditional methods often perform poorly in complex scenes. To address these issues, we propose an innovative approach that combines Low-Rank Tensor Recovery (LRTR) and Partially Support Vector Machine (PSVM) to achieve more efficient denoising and clarity enhancement. The low-rank tensor recovery technique effectively removes noise while preserving the structural integrity of the video. Meanwhile, the PSVM algorithm further optimizes video clarity through partial supervised learning. Experimental results show that, compared to traditional methods, this approach demonstrates significant advantages in various video scenes. It not only markedly improves denoising effects but also enhances detail retention and overall visual quality. This innovative method offers an efficient and reliable solution for the video processing field, with broad application prospects.