A camera array, an imaging device and/or a method for capturing image that employ a plurality of imagers fabricated on a substrate is provided. Each imager includes a plurality of pixels. The plurality of imagers include a first imager having a first imaging characteristics and a second imager having a second imaging characteristics. The images generated by the plurality of imagers are processed to obtain an enhanced image compared to images captured by the imagers. Each imager may be associated with an optical element fabricated using a wafer level optics (WLO) technology.

One embodiment of the present invention provides an image processing method, the method comprising: acquiring a plurality of viewpoint images; generating a contrast distribution from the plurality of viewpoint images; and generating an output image by performing image processing in accordance with the contrast distribution with respect to an image based on the plurality of viewpoint images.

A system and method for hologram synthesis and processing capable of synthesizing holographic 3D data and displaying (or reconstructing) a full 3D image at high speed using a deep learning engine. The system synthesizes or generates a digital hologram from a light field refocus image input using the deep learning engine. That is, RGB-depth map data is acquired at high speed using the deep learning engine, such as a convolutional neural network (CNN), from real 360° multi-view color image information and the RGB-depth map data is used to produce hologram content. In addition, the system interlocks hologram data with user voice recognition and gesture recognition information to display the hologram data at a wide viewing angle and enables interaction with the user.

An apparatus and a method are provided for image processing. In one embodiment, the method comprises accessing a plurality of images captured by at least a reference camera, wherein the images represent a plurality of views corresponding to said same scene. A plurality of plane sweep volume (PSV) slices are then generated from said images and computing for each slice a flow map from at least the reference camera calibration parameter and this flow map a previous slice of the plane sweep volume is generated.

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.

A method of an optical sensor device used for detecting a color of a specific surface of an object includes: providing a sensor array having a plurality of pixel units each for receiving light reflected from the specific surface to generate a sensed pixel value; using the sensor array to generate a specific image formed by a plurality of sensed pixel values; determining whether the specific image is similar to a first solid color image corresponding to a first color according to the plurality of sensed pixel values of the specific image; and determining to execute a distance detection operation to calculate a specific distance from the optical sensor device to the specific surface according to a color saturation value of the first color in the specific image when it is determined that the specific image is similar to the first solid color image corresponding to the first color.

Systems and methods for dynamically calibrating an array camera to accommodate variations in geometry that can occur throughout its operational life are disclosed. The dynamic calibration processes can include acquiring a set of images of a scene and identifying corresponding features within the images. Geometric calibration data can be used to rectify the images and determine residual vectors for the geometric calibration data at locations where corresponding features are observed. The residual vectors can then be used to determine updated geometric calibration data for the camera array. In several embodiments, the residual vectors are used to generate a residual vector calibration data field that updates the geometric calibration data. In many embodiments, the residual vectors are used to select a set of geometric calibration from amongst a number of different sets of geometric calibration data that is the best fit for the current geometry of the camera array.

A polarization imaging system for generating a super resolution image is disclosed in the present invention. The polarization imaging system consists of multiple functional elements for the generation of the super resolution image including a camera device, a polarization analysis module, a weight module, a fusion module and an output module. The lens of the imaging system captures light coming from a subject which is detected and acted upon by a polarization sensor. The polarization sensor generates multiple low resolution images and calculates degree of linear polarization and angle of polarization of the light rays based on which a guided mask is generated. The system employs an algorithm to combine the one or more low resolution images to produce a final super resolution image as the output.

There is provided an image processing device including: a camera outputting a first image obtained by photographing an object that is moving; and a control module generating a coded pattern for controlling a shutter exposure time and reconstructing a second image in which motion blur of the first image is removed, wherein the control module detects a moving speed of the object, and generates the coded pattern based on a point spread function (PSF) range set according to the moving speed.

A method for estimating a pose of an object includes: receiving, by a processor, an observed image depicting the object from a viewpoint; computing, by the processor, an instance segmentation map identifying a class of the object depicted in the observed image; loading, by the processor, a 3-D model corresponding to the class of the object; computing, by the processor, a rendered image of the 3-D model in accordance with an initial pose estimate of the object and the viewpoint of the observed image; computing, by the processor, a plurality of dense image-to-object correspondences between the observed image of the object and the 3-D model based on the observed image and the rendered image; and computing, by the processor, the pose of the object based on the dense image-to-object correspondences.