The method also uses temporal redundancy in real-world videos to enhance efficiency and temporal consistency. The enhanced frame is predicted using a straightforward decoder based on these tokens. Each set of tokens is passed through separate mixer layers to determine the dependencies between them. The authors tokenize the input frames in two ways to enable the network to learn both spatial and temporal features. The method’s uniqueness lies in its design, which employs convolutional and MLP-based blocks to avoid the high computational complexity associated with traditional attention mechanisms while maintaining good performance. The network takes in the frame that needs improvement and the previously predicted frame as input. With the help of neural networks, these tasks have become a reality. They recently have emerged as a powerful tool for video enhancement, allowing for unprecedented levels of clarity and detail in videos.Īmong the most exciting applications of neural networks in video enhancement exist super-resolution, which involves increasing the resolution of a video to provide a clearer and more detailed image, and denoising, which aims to turn blurry areas into distinguished features. One of the most promising video enhancement technologies is neural networks. Over the years, various video enhancement techniques have been developed until the arrival of complex machine learning algorithms to remove noise and improve image quality. In these situations, video enhancement techniques come into play, aiming to improve resolution and visual features. However, due to various factors like low light, digital noise, or simply low acquisition quality, the quality of videos captured by these devices is often far from perfect. With the increasing use of smartphones and other capture devices, the quality of videos has risen in importance. Videos have become omnipresent, from streaming our favorite movies and TV shows to participating in video conferences and calls.
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