Microscopy creates unavoidable light artifacts that are characteristic of the Point Spread Function (PSF) of an optical system. The PSF causes the haze and blur that makes image analysis difficult and inaccurate. Deconvolution with AutoDeblur reverses the effect of the PSF to restore image quality and accuracy, and produces an image with increased resolution, greater contrast and an improved signal-to-noise ratio.

AutoDeblur's core algorithms yield the most advanced, high-performing applications available on the market today. AutoDeblur provides:

• Both filtering and restorative algorithms
• Support of multiple microscope modalities
• Model-based advanced noise suppression
• Proprietary spherical aberration detection and correction module
• True adaptive 2D and 3D blind deconvolution

AutoDeblur is distributed by Bitplane and is tightly integrated into the Bitplane software suite.

Features Include:

(1) Pre-Processing
Autodeblur provides several options for correcting problems with your dataset prior to decolvolution

• Attenuation Correction – Corrects intensity attenuation as a function of depth into the sample

• Optical Density Correction – Corrects fluctuations in the image intensity values across the depth of an image

(2) Deconvolution Algorithms - Qualitative

• The 3D Inverse Filter - The inverse filter or "Wiener filter" is a one step image process performed in Fourier space that divides the captured image by the point spread function (PSF). The inverse filter algorithm is a fast and effective way to remove the majority of blur from widefield images. Image noise is managed through an adjustable smoothing operation applied during processing.

• The No/Nearest Neighbor - The no/nearest neighbor algorithms work by assuming that the out-of-focus contribution in the image to be deblurred is equal to a blurred version of the collected image slices that reside just above and below the target image. These algorithms work particularly well on images with strong signal to noise ratios.

• DIC Restoration - The DIC Restoration feature converts a DIC image into an image that represents the optical thickness of the specimen

(3) Deconvolution Algorithms - Quantitative

• Non-Blind Deconvolution - Non-blind deconvolution is a constrained iterative approach that requires a measured or synthetically acquired PSF for processing. This algorithm displays superior noise handling characteristics and flexibility. Non-blind offers an excellent balance between quality results, quantitative analysis and time to process.

• 3D - Adaptive-Blind Deconvolution - The adaptive-blind deconvolution algorithms draw upon the statistical techniques of Maximum Likelihood Estimation (MLE) and Constrained Iteration (CI) to produce the statistically accurate results. It is well suited for environments where signal to noise ratios are challenging and operates across the full spectrum of modalities.

• 2D – Adaptive Blind Deconvolution - The 2D algorithm is able to suppress noise while retaining quantitative accuracy (total number of photons) in the image, thus allowing you the ability to make valid quantitative measurements

(4) Optimizations

• Multiple Processors – The deconvolution process runs on as many processors / cores that are installed in the system with no additional charge for this functionality. This enables processing to be completed very quickly.

• Batch Processing – Available at no additional charge, this functionality allows a series of different images to be scheduled for processing. Different processing methods can easily be applied to different images as desired. This functionality allows processing to be completed overnight or when the system is not being used.

(5) Version - Options

• Wide-Field Only - DIC restoration, 2D and 3D deconvolution of wWide -field images

• Confocal Only – 2D and 3D deconvolution of confocal, multi-photon, and spinning disk images

• Confocal and Wide-Field – All options shown above