A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Systems and methods for removing objects from images are disclosed. An image processing application identifies a boundary of each object of a set of objects in an image. The image processing application identifies a completed boundary for each object of the set of objects by providing the object to a trained model. The image processing application determines a set of masks. Each mask corresponds to an object of the set of objects and represents a region of the image defined by an intersection of the boundary of the object and the boundary of a target object to be removed from the image. The image processing application updates each mask by separately performing content filling on the corresponding region. The image processing application creates an output image by merging each of the updated masks with portions of the image.

The present invention generally relates to an apparatus and a method for obtaining a registration error map representing a level of sharpness of an image. Many methods are known which allow determining the position of a camera with respect to an object, based on the knowledge of a 3D model of the object and the intrinsic parameters of the camera. However, regardless of the visual servoing technique used, there is no control in the image space and the object may get out of the camera field of view during servoing. It is proposed to obtain a registration error map relating to an image of the object of interest generated by computing an intersection of a re-focusing surface obtained from a 3D model of said object of interest and a focal stack based on acquired four-dimensional light-field data relating to said object of interest.

The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.

Disclosed herein are apparatuses, systems, methods, and computer-readable media relating to area selection in charged particle microscope (CPM) imaging. For example, in some embodiments, a CPM support apparatus may include: first logic to generate a first data set associated with an area of a specimen by processing data from a first imaging round of the area by a CPM; second logic to generate predicted parameters of the area; and third logic to determine whether a second imaging round of the area is to be performed by the CPM based on the predicted parameters of the area; wherein the first logic is to, in response to a determination by the third logic that a second imaging round of the area is to be performed, generate a second data set, including measured parameters, associated with the area by processing data from a second imaging round of the area by the CPM.

A system and method for an imaging circadiometer that measures the spatial distribution of eye-mediated, non-image-forming optical radiation within the visible spectrum.

A light field image processing method is disclosed for removing occluding foreground and blurring uninterested objects, by differentiating objects located at different depths of field and objects belonging to distinct categories, to create see-through effects. In various embodiments, the image processing method may blur a background object behind a specified object of interest. The image processing method may also at least partially remove from the rendered image any occluding object that may prevent a viewer from viewing the object of interest. The image processing method may further blur areas of the rendered image that represent an object in the light field other than the object of interest. The method includes steps of constructing a light field weight function comprising a depth component and a semantic component, where the weight function assigns a ray in the light field with a weight; and conducting light field rendering using the weight function.

Techniques image suitability determination to generate augmented reality/virtual reality (AR/VR) digital content are described. A two-dimensional digital image is received. Using machine-learning, a determination as to whether an object captured by the two-dimensional digital image is suitable for generating AR/VR digital content for display in an AR/VR environment is made. If the object is suitable, an indication is provided and an option to view the object in an AR/VR environment is provided. If the object is not suitable, a suggestion indicating why the object as captured is not suitable and/or how to correct the capture of the object in a subsequent digital image such that it is suitable for generating AR/VR digital content.

The present technology relates to an information processing device, an information processing method, a program, and an information processing system for enabling reduction in a load by enabling selective arithmetic processing in performing imaging without using an imaging lens. The information processing device includes an acquisition unit configured to acquire a detection image output from an imaging element that receives incident light incident without through an imaging lens, and restoration information including setting information set by a user and to be used to generate a restoration image from the detection image, a restoration processing unit configured to perform restoration processing of generating the restoration image using the detection image and the restoration information, and an output control unit configured to control an output of the restoration image. The present technology can be applied to, for example, a device or a system that restores a detection image captured by a lensless camera.

A method and system are provided for processing image content. In one embodiment the method comprises receiving a plurality of captured contents showing same scene as captured by one or more cameras having a different focal length and depth maps and generating a consensus cube by obtaining depth map estimations from said received contents. The visibility of different objects in then analysed to create a soft visibility cube that provides visibility information about each content. A color cube is then generated by using information from the consensus and soft visibility cube. The color cube is then used to combine different received contents and generate a single image for the plurality of contents received.