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  2. 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.

  3. Pixel-art scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Pixel-art_scaling_algorithms

    [16] xBR+3D is a version with a 3D mask that only filters 2D elements. xBRZ by Zenju is a modified version of xBR. It is implemented from scratch as a CPU-based filter in C++. [17] It uses the same basic idea as xBR's pattern recognition and interpolation but with a different rule set designed to preserve fine image details as small as a few ...

  4. OpenEXR - Wikipedia

    en.wikipedia.org/wiki/OpenEXR

    OpenEXR's library allows quick and easy access to the image's attributes such as tiles and channels. [6] The OpenEXR library is developed in C++ and is available in source format as well as compiled format for Microsoft Windows, macOS and Linux. Python bindings for the library are also available for version 2.x. [7]

  5. Image scaling - Wikipedia

    en.wikipedia.org/wiki/Image_scaling

    An image scaled with nearest-neighbor scaling (left) and 2×SaI scaling (right) In computer graphics and digital imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement.

  6. Kernel (image processing) - Wikipedia

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

    This can be described algorithmically with the following pseudo-code: for each image row in input image: for each pixel in image row: set accumulator to zero for each kernel row in kernel: for each element in kernel row: if element position corresponding* to pixel position then multiply element value corresponding* to pixel value add result to ...

  7. 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]

  8. ImageMagick - Wikipedia

    en.wikipedia.org/wiki/ImageMagick

    The software mainly consists of a number of command-line interface utilities for manipulating images. ImageMagick does not have a robust graphical user interface to edit images as do Adobe Photoshop and GIMP, but does include – for Unix-like operating systems – a basic native X Window GUI (called IMDisplay) for rendering and manipulating images and API libraries for many programming languages.

  9. Template matching - Wikipedia

    en.wikipedia.org/wiki/Template_matching

    Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [2] navigation of mobile robots, [3] or edge detection in images.