Blurring is present in any imaging system which uses electromagnetic radiation (for example, visible light and X-rays). Diffraction limits the resolution of an imaging device to features on the order of the illuminating wavelength. Scattering of light between the target object and imaging system (for example, by the atmosphere) introduces additional blurring. Lenses and mirrors cause blurring because they have limited spatial extent and optical imperfections. Discretization results in yet more blurring because devices such as CCDs average illumination over regions rather than sampling it at discrete points.
Noise is similarly omnipresent: any imaging device must use a finite exposure (or integration) time, which introduces stochastic noise from the random arrival of photons. Optical imperfections and instrumentation noise (for example, thermal noise in CCD devices) result in more noise. Sampling causes noise due to aliasing of high-frequency signal components, and digitization produces quantization errors. Further noise can be introduced by communication errors and compression.
Blurring and noise processes can be accurately approximated by mathematical models. The next sections review some common models for blurring and noise.