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AI Upscale Video Before and After

The power of AI is becoming more and more interesting. It's learning faster and can fix old blurry footage to HD.
3 min read

AI upscaling is a process in which a computer program uses artificial intelligence (AI) to improve the resolution of an image or video. This is typically done by analyzing the original image or video and using machine learning algorithms to generate additional pixels or frames that match the style and content of the original image or video. The result is a higher-resolution image or video that appears more detailed and lifelike than the original.

In the future, all the blurry and low-resolution footage will be a thing of the past, thanks to AI. Its amazing ability to upscale images and videos is very interesting. It can recreate faces of small images or blurry footage using Ai. Check this before and after the video.

AI upscaling is typically done using a technique called super-resolution, which involves generating additional pixels or frames that are similar to the pixels or frames in the original image or video. This is done by training a machine learning model on a large dataset of images or videos, and then using the model to generate additional pixels or frames for the image or video that is being upscaled. The model is able to do this by analyzing the patterns and features in the original image or video, and then using this information to create additional pixels or frames that are similar to the original.

Imagine in the next few years, it will only get better and smarter. Do you have an old family home video? Do you have some old movies you want to remaster? AI upscale will be able to do that and probably even better in the coming years.

AI upscaling can be useful in a variety of applications, such as improving the quality of low-resolution images or videos, enhancing the detail and clarity of images or videos for display on high-resolution screens, or converting images or videos from one resolution to another. AI upscaling can also be used to improve the performance of computer vision algorithms, as it can increase the amount of detail and information in an image or video, making it easier for the algorithms to detect and recognize objects and scenes.


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