The first public version of Krita was released with KOffice 1.4 in 2004. To avoid existing trademarks on the market, the project underwent numerous name changes: KImageShop, Krayon, until it was finally settled with 'Krita' in 2002. In 1999, Matthias Elter proposed the idea of building the software using CORBA around ImageMagick. The idea of building a Qt-based image editor was later passed to KImage, maintained by Michael Koch, as a part of KOffice suite. Conventional spatial filtering techniques for noise removal include: mean ( convolution) filtering, median filtering and Gaussian smoothing.Early development of the project can be tracked back to 1998 when Matthias Ettrich, founder of KDE, showcased a Qt GUI hack for GIMP at Linux Kongress. In digital image processing Gaussian noise can be reduced using a spatial filter, though when smoothing an image, an undesirable outcome may result in the blurring of fine-scaled image edges and details because they also correspond to blocked high frequencies. sensor noise caused by poor illumination and/or high temperature, and/or transmission e.g. Principal sources of Gaussian noise in digital images arise during acquisition e.g.

In telecommunications and computer networking, communication channels can be affected by wideband Gaussian noise coming from many natural sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson–Nyquist noise), shot noise, black-body radiation from the earth and other warm objects, and from celestial sources such as the Sun. In communication channel testing and modelling, Gaussian noise is used as additive white noise to generate additive white Gaussian noise. Ī special case is White Gaussian noise, in which the values at any pair of times are identically distributed and statistically independent (and hence uncorrelated). The probability density function p its standard deviation. In other words, the values that the noise can take are Gaussian-distributed. Gaussian noise, named after Carl Friedrich Gauss, is a term from signal processing theory denoting 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).

Krita gaussian noise reduction