An optical system has a virtual lens comprising an array of image sensors and processor in communication with the virtual lens and configured to focus the at least one virtual lens by way of mathematical image processing of a dynamic object plane.

The present technology relates to an image processing apparatus and method, a program, and an image processing system that enable identification of regions to be restored.

A restoration region where a restoration image is to be created by use of a restoration matrix is identified, the restoration region being in a region of a detection image obtained at an image capturing element that includes multiple pixels to receive incident beams that are incident thereon via neither an image capturing lens nor a pinhole and that is configured such that output pixel values of at least two pixels in the multiple pixels have mutually different characteristics in terms of angle-of-incidence directional sensitivities about incident beams from a subject. For example, the present disclosure can be applied to an image processing apparatus, an image capturing apparatus, an image capturing element, electronic equipment, a system, and the like.

An interval between a pinhole and a pinhole is set to a first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of a subject located at a distance less than a predetermined distance from the multi-pinhole camera is captured. An interval between the pinhole and a pinhole is set to a second interval narrower than the first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of the subject located at a distance equal to or more than the predetermined distance from the multi-pinhole camera is captured.

An interval between a pinhole and a pinhole is set to a first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of a subject located at a distance less than a predetermined distance from the multi-pinhole camera is captured. An interval between the pinhole and a pinhole is set to a second interval narrower than the first interval at which a degree of superimposition of subject images captured through the corresponding pinholes falls within a predetermined range when an image of the subject located at a distance equal to or more than the predetermined distance from the multi-pinhole camera is captured.

An image processing device includes a noise adder that obtains a captured image from an image capturing device including a mask having at least one aperture, an MPH information obtainer that obtains aperture pattern information corresponding to the pattern of the at least one aperture, the noise adder that adds, to the captured image, noise determined according to the aperture pattern information, and a transmitter that outputs the noise added captured image.

A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.

A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.

An optical sensor device may comprise an optical sensor comprising a set of sensor elements; an optical filter comprising one or more channels, wherein each channel, of the one or more channels, is configured to pass light associated with particular wavelengths to a subset of sensor elements, of the set of sensor elements, of the optical sensor; a phase mask configured to distribute a plurality of light beams associated with a subject in an encoded pattern on an input surface of the optical filter; and one or more processors. The one or more processors may be configured to obtain, from the optical sensor, sensor data associated with the subject and determine, based on the sensor data, spectral information associated with the subject. The one or more processors may determine, based on the sensor data and information associated with the encoded pattern, spatial information associated with the subject.

A method for manufacturing a phase mask and a lens-less camera module comprises the steps of: obtaining a replica mold on which an inverted phase shift pattern is formed in which a phase shift pattern of a master phase mask spaced apart from an image sensor is inverted; calculating a thickness of a phase mask disposed on the image sensor replacing the master phase mask; arranging a photocurable material for implementing the phase mask on the image sensor to a calculated thickness, placing the replica mold on an upper surface of the photocurable material, and then curing the photocurable material; and removing the replica mold from the top of the phase mask, so that the focal distance change or parallel movement does not occur depending on the position of the phase mask.

A method for manufacturing a phase mask and a lens-less camera module comprises the steps of: obtaining a replica mold on which an inverted phase shift pattern is formed in which a phase shift pattern of a master phase mask spaced apart from an image sensor is inverted; calculating a thickness of a phase mask disposed on the image sensor replacing the master phase mask; arranging a photocurable material for implementing the phase mask on the image sensor to a calculated thickness, placing the replica mold on an upper surface of the photocurable material, and then curing the photocurable material; and removing the replica mold from the top of the phase mask, so that the focal distance change or parallel movement does not occur depending on the position of the phase mask.