Scientific Volume Imaging b.v.
Blind deconvolution is an Image Restoration method that tries to obtain both the original object and the Point Spread Function (PSF) simultaneously out of the degraded image.
You can imagine that the image is formed in your microscope by replacing every original Sub Resolution light source by its correspondent Point Spread Function (PSF).
This process is mathematically described by a Convolution equation of the form
where the image g arises from the convolution of the real light sources f (the object) and the PSF h. (See Image Formation and Convolution for more details).
Restoring an image is, basically, solving the equation above, where you want to obtain the real object instensity distribution f given the acquired blurry image g: that is DeConvolution.
Blind Deconvolution tries to solve that equation without knowing the PSF term h. Although some constraints can be applied, this is always risky, as it introduces a lot of indetermination in the solution of the equation, specially if you let h freely vary all over the space.
How many solutions x, y can you find for an algebraical equation of the form x × y = 5? You can make x as small as you want by increasing the value of y, and vice versa. There is no determined solution, even if you put strong constraints. Something similar happens with solving the convolution equation: you can make the image f as clean as you wish (even totally washed out), just by making the PSF h stronger and stronger. But that does not imply that you are getting a good representation of your real object.
Blind deconvolution developed for astronomical images. As we may not know anything about the medium between the object and the telescope, we have limitations to know about the way the image is blurred. But in astronomy most of the objects are point-like, and this allows the application of strong constraints. Blind deconvolution is currently lacking of any scientific validation when applied to microscopy.
Read more on Huygens Deconvolution.
