![]() Using artificial intelligence to magnify an imageĪmong the most widely used techniques for image enlargement are the fractal compensation and use of artificial intelligence. Although quality will inevitably degrade with enlargement, there are techniques to minimize this degradation and achieve relatively good results. You cannot, for example, turn a small, low-resolution image into a high-quality poster. However, it is important to keep in mind the limitations of magnification. To maintain a consistent appearance and preserve image quality, these new pixels should adopt a color that matches that of neighboring pixels. In reality, during enlargement, new pixels are inserted. Understand that an image does not simply expand like a rubber band. The secret lies in the efficient filling of "gaps" when adding new pixels, ensuring that these harmonize with the pixels existing surroundings. However, with the advancement of artificial intelligence techniques, it is increasingly possible to minimize the loss of quality when enlarging images. It is therefore for these reasons that enlarging an image is often a trade-off between size and quality. This is mainly because removing pixels when downsizing makes pixel blocks less noticeable. Unfortunately, interpolation can lead to a blurry or distorted image, as shown in the image below:Ĭonversely, when you reduce the size of an image, the quality degradation is usually less noticeable. ![]() This process is called the the interpolation. However, since the information for these new pixels does not exist in the original image, the software must "invent" them based on the existing pixels. When you enlarge an image, you are essentially telling your software to add more pixels. ![]() Similarly, as you zoom or magnify a bitmap image, individual pixels become more and more visible, creating a pixelated effect. ![]() However, if you move closer or enlarge the mosaic, you will begin to see the individual tiles. From a distance, the pattern or image on the mosaic is clear. ![]()
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