There are several methods for increasing the resolution and enhancing an image using AI.

Let’s start by considering this scenario: Suppose you have a very low-resolution image of a face, such as a thumbnail, no larger than perhaps 10×10 pixels. AI can use this low-resolution thumbnail to generate a high-resolution image of a face that closely matches it. This is a fact. But the question is: have you really identified the correct person based on that 10×10 pixel thumbnail? The answer, of course, is no.

It has been mathematically proven that you cannot increase the details in an image without introducing more information. For example, you can slightly improve image quality by applying a sharpening algorithm, as it corrects known errors that are predictable. The image may even look like it has got higher resolution, but it really has not.  This doesnt’t stop us from using image sharpening. Think of AI as smart version of image sharpening and noise removal at once. Instead of just correcting sharpness, it may correct the image based on the patterns it already has seen before.

Now, let’s consider a different scenario: You can create a system that learns how objects like houses, trees, and grass look. It can learn the shape of a building’s corner, or recognize repeating patterns. Even without being explicitly trained to recognize letters, the system can learn about the alphabet. By training this system on real-life images, the algorithm will perform well when it needs to predict what colors to use for a pixel. In this case, more information is being added to the process by AI algorithms trained by you, which goes beyond mere guessing.

There are models designed to scale up images by 2x, 4x, and 8x. However, just like with sharpening algorithms, applying the scaling process multiple times does not lead to better results.