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stretching vs. ai upscaling in affinity photo 2: a comprehensive guide

this tutorial delves into the pros and cons of stretching images in affinity photo 2 compared to using ai upscaling techniques, specifically highlighting the use of ai upscaling tools with code examples (mainly python). we'll explore why simply stretching an image can lead to undesirable results and how ai upscaling offers a more sophisticated and visually appealing alternative.

*part 1: understanding the problem with stretching*

when you increase the size of an image by simply stretching it in affinity photo 2 (or any other image editing software), you are essentially taking the existing pixels and making them larger. no new information is being added. this leads to several problems:

*pixelation:* the most obvious issue. as pixels become larger, they become more visible, resulting in a blocky, jagged appearance. details are lost, and the image becomes noticeably blurred.
*loss of sharpness:* fine lines and edges become less defined, resulting in a soft and blurry image.
*artifacts:* stretching can accentuate existing artifacts (noise, compression artifacts) present in the original image.
*unnatural look:* the overall image can look unnatural and artificial, especially when upscaling significantly.

*how stretching works (in practice):*

affinity photo 2, like other raster graphics editors, uses various interpolation methods to determine the color of the new pixels it creates when you resize an image. here's a brief overview of common interpolation methods:

*nearest neighbor:* the simplest method. each new pixel takes the color of the nearest pixel in the original image. this results in a very pixelated look, especially for significant upscaling. fast but usually produces the worst visual quality.
*bilinear:* calculates the color of the new pixel by taking a weighted average of the four surrounding pixels in the original image. this smooths out the pixelati ...

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