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  2. Wikipedia:How to improve image quality - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:How_to_improve...

    Images can often be improved – or even transformed – by using graphics software such as Photoshop or the free GIMP and Paint.NET applications. Many other programs also have photo-enhancing tools, including facilities for semi-automatic image enhancement, so that you need only click a button, or choose a thumbnail, to have a positive effect on image quality.

  3. ClearType - Wikipedia

    en.wikipedia.org/wiki/ClearType

    Like most other types of subpixel rendering, ClearType involves a compromise, sacrificing one aspect of image quality (color or chrominance detail) for another (light and dark or luminance detail). The compromise can improve text appearance when luminance detail is more important than chrominance.

  4. Comparison gallery of image scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Comparison_gallery_of...

    The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.

  5. Image editing - Wikipedia

    en.wikipedia.org/wiki/Image_editing

    These parameters work together to produce a printed image of the desired size and quality. Pixels per inch of the image, pixel per inch of the computer monitor, and dots per inch on the printed document are related, but in use are very different. The Image Size dialog can be used as an image calculator of sorts.

  6. Wikipedia talk:How to improve image quality - Wikipedia

    en.wikipedia.org/wiki/Wikipedia_talk:How_to...

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  7. Text-to-image personalization - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_personalization

    Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model ), is adapted such that it can generate images of novel, user-provided concepts.

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