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  2. Comparison gallery of image scaling algorithms - Wikipedia

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

    Then the resolution-independent version is rendered as a raster image at the desired resolution. This technique is used by Adobe Illustrator Live Trace, Inkscape, and several recent papers. [6] Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity.

  3. Image restoration by artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Image_restoration_by...

    Image processing techniques are performed either in the image domain or the frequency domain. The most straightforward and a conventional technique for image restoration is deconvolution , which is performed in the frequency domain and after computing the Fourier transform of both the image and the PSF and undo the resolution loss caused by the ...

  4. Pixel-art scaling algorithms - Wikipedia

    en.wikipedia.org/wiki/Pixel-art_scaling_algorithms

    These algorithms are a form of automatic image enhancement. Pixel art scaling algorithms employ methods significantly different than the common methods of image rescaling, which have the goal of preserving the appearance of images. As pixel art graphics are commonly used at very low resolutions, they employ careful coloring of individual pixels ...

  5. Super-resolution imaging - Wikipedia

    en.wikipedia.org/wiki/Super-resolution_imaging

    These methods use other parts of the low resolution images, or other unrelated images, to guess what the high-resolution image should look like. Algorithms can also be divided by their domain: frequency or space domain. Originally, super-resolution methods worked well only on grayscale images, [18] but researchers have found methods to adapt ...

  6. Pyramid (image processing) - Wikipedia

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

    A Laplacian pyramid is very similar to a Gaussian pyramid but saves the difference image of the blurred versions between each levels. Only the smallest level is not a difference image to enable reconstruction of the high resolution image using the difference images on higher levels. This technique can be used in image compression. [13]

  7. Deep image prior - Wikipedia

    en.wikipedia.org/wiki/Deep_Image_Prior

    Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction , super-resolution , and inpainting .

  8. Drizzle (image processing) - Wikipedia

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

    Drizzle (or DRIZZLE) is a digital image processing method for the linear reconstruction of undersampled images. The method is normally used for the combination of astronomical images and was originally developed for the Hubble Deep Field observations made by the Hubble Space Telescope.

  9. Multiresolution analysis - Wikipedia

    en.wikipedia.org/wiki/Multiresolution_analysis

    In the sequence of subspaces, for k>l the space resolution 2 l of the l-th subspace is higher than the resolution 2 k of the k-th subspace. Regularity demands that the model subspace V 0 be generated as the linear hull ( algebraically or even topologically closed ) of the integer shifts of one or a finite number of generating functions ϕ ...