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  2. Latent diffusion model - Wikipedia

    en.wikipedia.org/wiki/Latent_Diffusion_Model

    The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.

  3. Gaussian noise - Wikipedia

    en.wikipedia.org/wiki/Gaussian_noise

    In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1] [2] In other words, the values that the noise can take are Gaussian-distributed.

  4. Geometric mean filter - Wikipedia

    en.wikipedia.org/wiki/Geometric_mean_filter

    Each pixel of the output image at point (x,y) is given by the product of the pixels within the geometric mean mask raised to the power of 1/mn. For example, using a mask size of 3 by 3, pixel (x,y) in the output image will be the product of S(x,y) and all 8 of its surrounding pixels raised to the 1/9th power.

  5. Diffusion model - Wikipedia

    en.wikipedia.org/wiki/Diffusion_model

    Each image is a point in the space of all images, and the distribution of naturally-occurring photos is a "cloud" in space, which, by repeatedly adding noise to the images, diffuses out to the rest of the image space, until the cloud becomes all but indistinguishable from a Gaussian distribution (,). A model that can approximately undo the ...

  6. Gaussian filter - Wikipedia

    en.wikipedia.org/wiki/Gaussian_filter

    By averaging pixel values with a weighted Gaussian distribution, the filter effectively blurs the image, diminishing high-frequency noise. [12] Edge Detection: Gaussian filters are often used as a preprocessing step in edge detection algorithms. By smoothing the image, they help to minimize the impact of noise before applying methods like the ...

  7. Bilateral filter - Wikipedia

    en.wikipedia.org/wiki/Bilateral_filter

    Left: original image. Right: image processed with bilateral filter. A bilateral filter is a non-linear, edge-preserving, and noise-reducing smoothing filter for images. It replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels. This weight can be based on a Gaussian distribution.

  8. Image noise - Wikipedia

    en.wikipedia.org/wiki/Image_noise

    Noise visible in an image from a digital camera. Image noise is random variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the image sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ...

  9. Edge-preserving smoothing - Wikipedia

    en.wikipedia.org/wiki/Edge-preserving_smoothing

    Anisotropic diffusion generates small conductance at the location of the edge of the image to prevent the heat flow over the edge, thus making the anisotropic diffusion filter edge-preserving. In the graph-based interpretation, the small conductance corresponds to a small weight of an edge of the graph describing a probability of a random walk ...

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