Video super-resolution using neural networks is a sophisticated computational task that involves increasing the resolution of video frames through artificial intelligence. This process utilizes deep learning models, particularly Convolutional Neural Networks (CNNs), to predict high-resolution details from low-resolution inputs.
The neural network is trained on a dataset of videos to learn the mapping between low and high-resolution images. During this training, it identifies and learns patterns and textures that are common in high-resolution imagery. Once trained, the neural network applies this learned information to enhance the resolution of new video frames, effectively 'filling in' the missing details that were not captured by the original lower-resolution recording.
This results in videos that are not only clearer and sharper but also closer to the quality that might be captured with high-definition cameras, all achieved through the power of machine learning and neural network processing.