A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

A system and a method for measuring the focus state of an optical instrument are described. The system and the method provide for forming an image of the exit pupil of the objective of the optical instrument. Where the image of the exit pupil is formed, one or more optical elements are placed, deviating at least part of the rays coming from the exit pupil, so that rays coming from different non-overlapping portions of the pupil follow separate optical paths. The rays coming from the two portions of the pupil are then focused, in order to obtain two bidimensional images. A computer determines the mutual distance between these two bidimensional images as a mutual rigid lateral displacement between the two and, based on this distance, determines the corresponding defocus of the optical instrument.

Embodiments disclose a device for testing biological specimen. The device includes a sample carrier and a detachable cover. The sample carrier includes a specimen holding area. The detachable cover is placed on top of the specimen holding area. The detachable cover includes a magnifying component configured to align with the specimen holding area. The focal length of the magnifying component is from 0.1 mm to 8.5 mm. The magnifying component has a linear magnification ratio of at least 1. Some embodiments further include a multi-camera configuration. These embodiments include a first camera module and a second camera module arranged to capture one or more images of the first holding area and the second holding area, respectively. The processor may perform different analytic processes on the captured images of different holding areas to determine an outcome with regard to the biological specimen.

A microscope includes a holder for holding a sample, an objective for imaging at least apart of a sample held by the holder, a detection module, a control unit for setting the focus position of the objective in a first direction for the recording by means of the detection module, and a focusing module for maintaining a set focus position of the objective. The focusing module includes the control unit, a second detector and first focusing optics with adjustable focal length. The focusing module is switchable into a focus-hold mode, wherein an intensity-modulated object is imaged into the sample via the first focusing optics and the objective, and an image of the imaged object is recorded by means of the second detector. The control unit holds the focus position of the objective on the set focus position, based upon the recording of the second detector.

The disclosure relates to methods and systems for automatically focusing multiple images of one or more objects on a substrate. The methods include obtaining, by a processor, a representative focal distance for a first location on the substrate based on a set of focal distances at known locations on the substrate. The methods also include acquiring, by an image acquisition device, a set of at least two images of the first location. The images are each acquired using a different focal distance at an offset from the representative focal distance. The methods further include estimating, by a processor, an ideal focal distance corresponding to the first location based on comparing a quality of focus for each of the images, and storing the estimated ideal focal distance and the first location in the set of focal distances at known locations.

Certain configurations are described of methods and systems that can be used to image three-dimensional objects such as biological cells, biological tissues or biological organisms. The methods and systems can image the three-dimensional objects at reduced imaging times and with reduced data volumes.

A microscope (10) is described, having an autofocus system (11) for executing a focusing procedure, having a first image sensor (14a), arranged in a first outcoupled beam path (12a), for acquiring a first image (16a); and a second image sensor (14b), arranged in a second outcoupled beam path (12b), for acquiring a second image (16b). The autofocus system (11) is embodied to ascertain a contrast difference based on contrast values of the first image (16a) acquired by the first image sensor (14a) and of the second image (16b) acquired by the second image sensor (14b), and to set a relative location of the focal plane (20) with respect to the object plane (22) based on the ascertained contrast difference, the first and the second image (16a, 16b) each encompassing image information furnished by the first and the second image sensor (14a, 14b) each embodied as an area sensor.

A microscope is provided. The microscope includes a lens system comprising a lens unit, which is adjustable along an optical axis of the lens system to correct an imaging error. The microscope further includes a motor-actuatable adjustment device, which is configured to adjust the lens unit along the optical axis. The microscope also includes a processor and a scanning unit, which is configured to deflect a light beam used for the image recording. The processor is configured to compare a position of an image which has been recorded after a correction adjustment of the lens unit to reference data, detect a change of the position of the image due to the correction adjustment of the lens unit based on the comparison, and activate the scanning unit in such a way that the change of the position of the image is at least partially compensated for.

The present disclosure provides method and system for auto focusing a microscopic imaging system using machine learned regression system such as Convolutional Neural Network (CNN). The method comprises receiving a first image of a sample under review and a second image of sample wherein the first image is captured at first focus position and second image is captured at second focus position. The CNN is trained using the plurality of historic difference images along with direction of focus and optimal focus position. The difference of two images are obtained in terms of difference in pixel values. The direction of focus and optimal focus position for difference image is identified based on plurality of historic difference images along with direction of focus and optimal focus position. The method enables automated stage comprising sample to move towards direction of focus and position at optimal focus position for capturing a focused image.

A microscope for computational imaging may include an illumination source configured to illuminate a sample with a plurality of wavelengths, an image sensor, an objective lens to image the sample onto the image sensor, and a processor operatively coupled to the illumination assembly and the image sensor. The processor may be configured to acquire a first image dataset from the sample illuminated using a first set of illumination conditions at a first wavelength. The processor may also be configured to acquire a second image dataset from the sample illuminated using a second set of illumination conditions having a second number of illumination conditions at a second wavelength. The second set of illumination conditions comprises fewer illumination conditions than the first set in order to decrease acquisition time. The processor may be configured to combine the first and second image datasets into a computationally reconstructed image of the sample.