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  2. Comparison gallery of image scaling algorithms - Wikipedia

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

    One of the simpler ways of increasing the size, replacing every pixel with a number of pixels of the same color. 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 ...

  3. Python Imaging Library - Wikipedia

    en.wikipedia.org/wiki/Python_Imaging_Library

    Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]

  4. Docker (software) - Wikipedia

    en.wikipedia.org/wiki/Docker_(software)

    Docker clients connect to registries to download ("pull") images for use or upload ("push") images that they have built. Registries can be public or private. The main public registry is Docker Hub. Docker Hub is the default registry where Docker looks for images. [23] [27] Docker registries also allow the creation of notifications based on ...

  5. Seam carving - Wikipedia

    en.wikipedia.org/wiki/Seam_carving

    Original image to be made narrower Scaling is undesirable because the castle is distorted. Cropping is undesirable because part of the castle is removed. Seam carving. Seam carving (or liquid rescaling) is an algorithm for content-aware image resizing, developed by Shai Avidan, of Mitsubishi Electric Research Laboratories (MERL), and Ariel Shamir, of the Interdisciplinary Center and MERL.

  6. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    This method can result in the output image being slightly smaller, with the edges having been cropped. Move kernel so that values from outside of image is never required. Machine learning mainly uses this approach. Example: Kernel size 10x10, image size 32x32, result image is 23x23. Kernel Crop

  7. Mortgage and refinance rates for Dec. 23, 2024: Holiday week ...

    www.aol.com/finance/mortgage-and-refinance-rates...

    The holiday week opens with another rise in average rates on 30-year and 15-year fixed-term mortgages, trending higher after the Federal Reserve announced a third straight cut to its benchmark ...

  8. These Are the Healthiest Fast Food Restaurants, According to ...

    www.aol.com/healthiest-fast-food-restaurants...

    Fast food gets a bad rap for being unhealthy, but there are healthy fast food options at chains like McDonald’s, Pizza Hut, and Sonic. Dietitians explain.

  9. Run-length encoding - Wikipedia

    en.wikipedia.org/wiki/Run-length_encoding

    In 1983, run-length encoding was patented by Hitachi. [2] [3] [4] RLE is particularly well suited to palette-based bitmap images (which use relatively few colours) such as computer icons, and was a popular image compression method on early online services such as CompuServe before the advent of more sophisticated formats such as GIF. [5]