Deconvolution is a mathematical operation used in Image Restoration to recover an object from an image that is degraded by blurring and noise. In fluorescence microscopy the blurring is largely due to diffraction limited imaging by the instrument (see Image Formation); the noise is usually Photon Noise.

All images are subject to some sort of degrading process. Consider as a 2D example a moving camera that creates a vague picture (the measured image) of a scene (the object). Here the camera displacement is an a priori known degrading process. Restoration applies the inverse process on the degraded image in order to recover the true object. In microscopy we apply the same technique on 3D images where the degrading process is the diffraction and aberration of the microscope lens.

Image Restoration is different from Image Enhancement!!!

For some accessible examples see Convolving Trains. More examples in Restoration Examples.

Special topics



Support on Huygens