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.
- Convolution and DeConvolution
- Huygens Deconvolution
- Basic concepts About Microscopy
- Image Restoration
- Resolution Improvement
- Restoration Examples
- Important Factors to record good images and do deconvolution.
- Acquisition Pitfalls: frequent problems using microscopes.
- Theo Vs Exp Psf: comparison between experimental and theoretical PSF in a typical case, and some guidelines for a good PSF acquisition.
- Recording Beads: first steps to obtain an experimental PSF of your microscope.
- Deconvolving Beads: typical test for deconvolution
- Deconvolution For Non Optical Images: can you use the Huygens Software with microscopes other than 3D optical?
- Colocalization improvement: see Colocalization Theory, and references therein.
Support on Huygens
- General Deconvolution Procedure
- Send Images To Svi if you need assistance with deconvolution
- Other Tutorials
- Personal Deconvolution Assistance