The present disclosure relates to an image processing apparatus, an image processing method, and a program capable of realizing highly accurate image recognition processing while taking privacy into consideration with a simple configuration. A mask of a lensless camera is convoluted with a weight of a DNN first layer used in image recognition processing using a DNN, and a reconstructed image becomes a processing result of the DNN first layer on the basis of a captured image captured by an image sensor. Accordingly, the restored image becomes the processing result of the DNN first layer and becomes an image difficult for a person to visually recognize as a scene or an object, making it possible to protect the privacy of the image. The present disclosure can be applied to an image recognition apparatus using a lensless camera.

There is provided an adjustment device and an operation method of the adjustment device that enable a positional relationship between a mask and an imaging element in a lensless camera to be adjusted with high accuracy and in a simple manner. When white light as incident light passes through a mask including a transmissive region including a plurality of condenser elements and a light-shielding region and provided at a preceding stage of an imaging element, the mask is adjusted to establish an appropriate positional relationship between the mask and the imaging element on the basis of a color of light corresponding to a wavelength of an image captured by the imaging element. The present disclosure is applicable to a calibration device for a mask and an imaging element in a lensless camera.

There is provided an adjustment device and an operation method of the adjustment device that enable a positional relationship between a mask and an imaging element in a lensless camera to be adjusted with high accuracy and in a simple manner. When white light as incident light passes through a mask including a transmissive region including a plurality of condenser elements and a light-shielding region and provided at a preceding stage of an imaging element, the mask is adjusted to establish an appropriate positional relationship between the mask and the imaging element on the basis of a color of light corresponding to a wavelength of an image captured by the imaging element. The present disclosure is applicable to a calibration device for a mask and an imaging element in a lensless camera.

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.

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.

Implementations disclosed herein include a method of imaging an albedo of an astronomical object in space. The method includes arranging an imaging system in space with an orientation facing the object. The imaging system includes a lensless image sensor with the orientation that captures images of the object. The method also includes maintaining the imaging system with the orientation facing the object during an imaging data capture session, and it includes capturing near-continuous images with the imaging system during the imaging data capture session. The orientation is maintained for subsequent image data capture sessions. For some implementations, the imaging system includes a lensed image sensor, and capturing the near-continuous images includes simultaneously capturing time-correlated images of the object with the lensed image sensor and the lensless image sensor.

Implementations disclosed herein include a method of imaging an albedo of an astronomical object in space. The method includes arranging an imaging system in space with an orientation facing the object. The imaging system includes a lensless image sensor with the orientation that captures images of the object. The method also includes maintaining the imaging system with the orientation facing the object during an imaging data capture session, and it includes capturing near-continuous images with the imaging system during the imaging data capture session. The orientation is maintained for subsequent image data capture sessions. For some implementations, the imaging system includes a lensed image sensor, and capturing the near-continuous images includes simultaneously capturing time-correlated images of the object with the lensed image sensor and the lensless image sensor.

There is provided an imaging device capable of improving the image quality of a reconstructed image in lensless imaging, and a method for operating the imaging device. In lensless imaging, a selection filter is provided that, for each of a plurality of regions into which an imaging surface on the imaging element is divided, allows only the modulated light having optical characteristics that are orthogonal to each other between the regions adjacent to each other, to pass through. With a sub-area set for each selection filter as a unit, the modulation mask converts the incident light into modulated light having optical characteristics corresponding to the selection filter based on at least one of polarization and spectroscopy. The present disclosure can be applied to an imaging device.

There is provided an imaging device capable of improving the image quality of a reconstructed image in lensless imaging, and a method for operating the imaging device. In lensless imaging, a selection filter is provided that, for each of a plurality of regions into which an imaging surface on the imaging element is divided, allows only the modulated light having optical characteristics that are orthogonal to each other between the regions adjacent to each other, to pass through. With a sub-area set for each selection filter as a unit, the modulation mask converts the incident light into modulated light having optical characteristics corresponding to the selection filter based on at least one of polarization and spectroscopy. The present disclosure can be applied to an imaging device.

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.