Scientific Volume Imaging b.v.

Scientific Volume Imaging b.v.
Laapersveld 63
1213 VB Hilversum, The Netherlands
http://www.svi.nl
http://support.svi.nl/wiki

Image restoration

Image restoration refers to the recovery of an original signal from degraded observations.

Image restoration is different from image enhancement in that the latter is designed to emphasize features of the image that make the image more pleasing to the observer, but not necessarily to produce realistic data from a scientific point of view. Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by "Imaging packages" use no a priori model of the process that created the image.

With image enhancement noise can be effectively be removed by sacrificing some resolution, but this is not acceptable in many applications. In a Fluorescence Microscope resolution in the z-direction is bad as it is. More advanced image processing techniques must be applied to recover the object.

DeConvolution is an example of image restoration method. It is capable of:

• Removing noise
• Increasing contrast
• Increasing resolution, especially in the axial direction

Since axial imaging performance is the prime reason for researchers to invest in expensive optical equipment like confocal or two-photon excitation microscopes, the capability of increasing axial resolution with a 'mere' software technique has considerable value.

Examples

Chromosomes in sea urchin nucleus recorded with a confocal microscope. Left: raw image data. Middle: filtered and enhanced data. Right: after image restoration. Top row: XY plane at z = 38; bottom row: XZ plane at y = 229. Image dimensions: 22.5 × 22.5 × 11.4 µm. Data courtesy Prof. Thomas Baechi, University Zurich, Switzerland.

See larger image: imageRestoration.png (471 kB 2008-09-15 14:53)

Chromosomes in sea urchin nucleus recorded with a confocal microscope. Left: Simulated Fluorescence Process (SFP) rendering of the raw image after thresholding the background. Middle: SFP rendering after applying a 3D gaussian noise filter and a Soft Threshold. Right: SFP after applying image restoration with the Huygens Software. Data courtesy Prof. Thomas Baechi, University Zurich, Switzerland.

See larger image: imageRestorationSFP.png (511 kB 2008-09-15 14:53)

See other examples in Restoration Examples.