Lucent Technologies Inc.

Max-planck-gesellschaft Zur F&#xf6;rderung Der Wissenschaften E. V.

Massachusetts Institute of Technology

Winfried Denk

Michale Sean Fee

David William Tank

Fritjof Helmchen

Srinivas C Turaga

H Sebastian Seung

Viren Jain

Joseph F Murray

Moritz Helmstaedter

Marcus Feierabend

Markus Rückel

Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.

Method and apparatus for image processing

A method for sensing a wave-front of specimen light scattered from an illuminated area in a specimen () includes the steps of focusing illumination light into the specimen (), directing specimen light scattered in the specimen () to a detector device () having a plurality of detector elements () and being capable to sense light with local resolution, detecting sample light contained in the specimen light with the detector device (), said sample light being scattered in a predetermined sample plane () of the specimen () and being selected by a time-based gating of the specimen light, locally resolved measuring phase information of the sample light, and reconstructing the wave-front of the sample light on the basis of the phase information. Furthermore, a method of microscopic imaging with adapted illumination light is described.

Method and device for wave-front sensing

A process for operating an optical scanning system includes making an image of a sample by scanning spots in the sample, measuring intensities of light emitted by the scanned spots, determining locations of the scanned spots, and assigning intensities to image pixels based on the measured intensities and determined locations of the scanned spots. In the making of the image, the acts of determining depend on a value of a parameter. The process also includes selecting a new value for the parameter and deciding whether the image of the sample has less double imaging if the new value of the parameter is used during the acts of determining. The process accepts the new value of the parameter in response to determining that the new value produces less of the double imaging.

System and method for optical scanning

An optical scanning system includes a probe and a processor. The probe includes a mechanical oscillator responsive to AC voltage signals and an optical fiber. The optical fiber has a free end that executes an oscillatory scanning motion in response to being mechanically driven by the mechanical oscillator. The processor is configured to receive measured intensities of light emitted from spots of a sample scanned by light from the free end of the optical fiber. The processor is also configured to assign intensities to image pixels based on the measured intensities of light. The acts of assigning compensate for variations in the density of the scanned spots.

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Heidelberg, Germany

Winfried Denk