Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.

The present invention provides an image processing method, including: obtaining a first image using a camera under a display screen; processing the first image using a processor; obtaining a second image using the camera under the display screen; processing the second image using the processor; and generating a superimposed image after superimposing the first sub-image and the second sub-image.

The present invention provides an image processing method, including: obtaining a first image using a camera under a display screen; processing the first image using a processor; obtaining a second image using the camera under the display screen; processing the second image using the processor; and generating a superimposed image after superimposing the first sub-image and the second sub-image.

There is provided an image pickup device and an image pickup method for estimating the depth of an image having a repetitive pattern with high accuracy. The peripheral cameras are arranged according to base line lengths based on reciprocals of different prime numbers as having a position of a reference camera, to be a reference when images from different viewpoints are imaged, as a reference. The present disclosure is capable of being applied to a light field camera and the like, for example, which includes the reference camera and the plurality of peripheral cameras, generates a parallax image from the images of plural viewpoints, and generates a refocus image by using the images from the plural viewpoints and the parallax image.

Provided is an electronic device, wherein the at least one processor is configured to execute the at least one instruction to obtain a first light field image including view images with a first number of views, obtain, from the first light field image, a second light field image including view images with a second number of views, obtain first location information corresponding to each of sub-pixels in the second light field image, obtain a first layer image by inputting the second light field image and the first location information to an artificial intelligence model configured to perform factorization, obtain a third light field image including view images with a third number of views by inputting the first layer image to a simulation model, and train the artificial intelligence model, based on a result of comparing the first light field image and the third light field image with each other.

Methods of determining the depth of a scene and associated systems are disclosed herein. In some embodiments, a method can include augmenting depth data of a scene captured with a depth sensor with depth data from one or more images of the scene. For example, the method can include capturing image data of the scene with a plurality of cameras. The method can further include generating a point cloud representative of the scene based on the depth data from the depth sensor and identifying a missing region of the point cloud, such as a region occluded from the view of the depth sensor. The method can then include generating depth data for the missing region based on the image data. Finally, the depth data for the missing region can be merged with the depth data from the depth sensor to generate a merged point cloud representative of the scene.

Methods of determining the depth of a scene and associated systems are disclosed herein. In some embodiments, a method can include augmenting depth data of a scene captured with a depth sensor with depth data from one or more images of the scene. For example, the method can include capturing image data of the scene with a plurality of cameras. The method can further include generating a point cloud representative of the scene based on the depth data from the depth sensor and identifying a missing region of the point cloud, such as a region occluded from the view of the depth sensor. The method can then include generating depth data for the missing region based on the image data. Finally, the depth data for the missing region can be merged with the depth data from the depth sensor to generate a merged point cloud representative of the scene.

A computer-implemented method including: capturing visible-light images via visible-light camera(s) from view points in real-world environment, wherein 3D positions of view points are represented in coordinate system; dividing 3D space of real-world environment into 3D grid of convex-polyhedral regions; creating 3D data structure including nodes representing convex-polyhedral regions of 3D space; determining 3D positions of pixels of visible-light images based on 3D positions of view points; dividing each visible-light image into portions, wherein 3D positions of pixels of given portion of said visible-light image fall inside corresponding convex-polyhedral region; and storing, in each node, portions of visible-light images whose pixels' 3D positions fall inside corresponding convex-polyhedral region, wherein each portion of visible-light image is stored in corresponding node.

A computer-implemented method including: capturing visible-light images via visible-light camera(s) from view points in real-world environment, wherein 3D positions of view points are represented in coordinate system; dividing 3D space of real-world environment into 3D grid of convex-polyhedral regions; creating 3D data structure including nodes representing convex-polyhedral regions of 3D space; determining 3D positions of pixels of visible-light images based on 3D positions of view points; dividing each visible-light image into portions, wherein 3D positions of pixels of given portion of said visible-light image fall inside corresponding convex-polyhedral region; and storing, in each node, portions of visible-light images whose pixels' 3D positions fall inside corresponding convex-polyhedral region, wherein each portion of visible-light image is stored in corresponding node.

Method and device for generating a stream from image(s) of an object is disclosed. The method includes the steps of obtaining data associated with points of a point cloud representing at least a part of the object, obtaining a parametric surface according to at least a geometric characteristic associated with the at least a part of the object and pose information of an acquisition device used to acquire the at least one image, obtaining a height map and one or more texture maps associated with the parametric surface, and generating the stream by combining together a first syntax element relative to the at least a parameter, a second syntax element relative to the height map, a third syntax element relative to the at least one texture map and a fourth syntax element relative to a position of the acquisition device. The disclosure relates further to a method and device for rendering an image of the object from the stream thus obtained.