A communication technique for merging, with an IoT technology, a 5G communication system for supporting a data transmission rate higher than that of a 4G system is provided. The communication technique can be applied to an intelligent service (for example, smart home, smart building, smart city, smart car or connected car, health care, digital education, retail business, and security and safety-related services, and the like) on the basis of a 5G communication technology and an IoT-related technology. A method for processing a 360-degree image is provided. The method includes determining a three-dimensional (3D) model for mapping a 360-degree image; determining a partition size for the 360-degree image; determining a rotational angle for each of the x, y, and z axes of the 360-degree image; determining an interpolation method to be applied when mapping the 360-degree image to a two-dimensional (2D) image; and converting the 360-degree image into the 2D image.

A communication technique for merging, with an IoT technology, a 5G communication system for supporting a data transmission rate higher than that of a 4G system is provided. The communication technique can be applied to an intelligent service (for example, smart home, smart building, smart city, smart car or connected car, health care, digital education, retail business, and security and safety-related services, and the like) on the basis of a 5G communication technology and an IoT-related technology. A method for processing a 360-degree image is provided. The method includes determining a three-dimensional (3D) model for mapping a 360-degree image; determining a partition size for the 360-degree image; determining a rotational angle for each of the x, y, and z axes of the 360-degree image; determining an interpolation method to be applied when mapping the 360-degree image to a two-dimensional (2D) image; and converting the 360-degree image into the 2D image.

An operation method of an electronic device is provided. The method includes generating a first bokeh image by blurring a part of an image acquired using at least one of a first camera module or a second camera module, by using a distance value determined based on the first camera module and the second camera module, and identifying whether a designated subject is included in the acquired image by using at least one of the first camera module or the second camera module; and in response to identifying that the designated subject is included, deactivating the second camera module, performing image segmentation on the acquired image by using the first camera module, and generating a second bokeh image by blurring a part of the acquired image, based on a result of the image segmentation.

An immersive display and a method of operating the immersive display to provide information relating to an object. The method includes receiving information from an input device of the immersive display or coupled to the immersive display, detecting an object based on the information received from the input device, and displaying a representation of the object on images displayed on a display of the immersive display such that attributes of the representation distinguish the representation from the images displayed on the display, wherein the representation is displayed at a location on the display that corresponds with a location of the object.

An immersive display and a method of operating the immersive display to provide information relating to an object. The method includes receiving information from an input device of the immersive display or coupled to the immersive display, detecting an object based on the information received from the input device, and displaying a representation of the object on images displayed on a display of the immersive display such that attributes of the representation distinguish the representation from the images displayed on the display, wherein the representation is displayed at a location on the display that corresponds with a location of the object.

A digital loupe system is provided which can include a number of features. In one embodiment, the digital loupe system can include a stereo camera pair and a distance sensor. The system can further include a processor configured to perform a transformation to image signals from the stereo camera pair based on a distance measurement from the distance sensor and from camera calibration information. In some examples, the system can use the depth information and the calibration information to correct for parallax between the cameras to provide a multi-channel image. Ergonomic head mounting systems are also provided. In some implementations, the head mounting systems can be configurable to support the weight of a digital loupe system, including placing one or two oculars in a line of sight with an eye of a user, while improving overall ergonomics, including peripheral vision, comfort, stability, and adjustability. Methods of use are also provided.

Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.

Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.

An apparatus for generating view images for a scene comprises a store (101) which stores three dimensional scene data representing the scene from a viewing region. The three dimensional scene data may e.g. be images and depth maps captured from capture positions within the viewing region. A movement processor (105) receives motion data, such as head or eye tracking data, for a user and determines an observer viewing position and an observer viewing orientation from the motion data. A change processor (109) determines an orientation change measure for the observer viewing orientation and an adapter (111) is arranged to reduce a distance from the observer viewing position relative to the viewing region in response to the orientation change measure. An image generator (103) generates view images for the observer viewing position and the observer viewing orientation from the scene data.

Methods and systems for capturing a three dimensional image are described. An image capture process is performed while moving a lens to capture image data across a range of focal depths, and a three dimensional image reconstruction process generates a three dimensional image based on the image data. A two-dimensional image is also rendered including focused image data from across the range of focal depths. The two dimensional image and the three dimensional image are fused to generate a focused three dimensional model.