Deconvolviert

Deconvolviert is the German past participle of "deconvolvieren" (to deconvolve) and is used to describe data, images or signals that have undergone a deconvolution process. In technical contexts it denotes an output from algorithms intended to reverse the effects of convolution—typically the blurring or smearing introduced by a measurement system—by estimating the original signal or scene and the system's impulse response.

Deconvolution is mathematically ill-posed in the presence of noise because direct inversion tends to amplify measurement errors. Practical methods therefore incorporate regularization or statistical priors. Common algorithms include inverse filtering, Wiener filtering, Richardson–Lucy iteration, maximum likelihood or Bayesian approaches, and blind deconvolution techniques that estimate both the underlying signal and the point spread function (PSF).

The term deconvolviert appears frequently in disciplines where restoring resolution or temporal detail is important, such as astronomy and astrophotography, fluorescence and electron microscopy (deconvolution microscopy), seismic imaging, remote sensing, audio signal restoration, and mass spectrometry. Results labeled as deconvolviert typically come with caveats about artifacts, residual noise, and dependence on algorithm parameters or assumed PSFs.

Related concepts include convolution, point spread function, deblurring, regularization, and inverse problems. The choice of deconvolution method and parameterization depends on the noise characteristics, the availability of an accurate system model, and the desired balance between resolution recovery and artifact suppression.