Abstract: A method includes obtaining, using at least one processor of an electronic device, multiple video frames of a video stream and multiple depth frames corresponding to the multiple video frames. The method also includes generating, using the at least one processor, multiple blur kernel maps based on the multiple depth frames. The method further includes reducing, using the at least one processor, depth errors in each of the multiple blur kernel maps. The method also includes performing, using the at least one processor, temporal smoothing on the multiple blur kernel maps to suppress temporal artifacts between different ones of the multiple blur kernel maps. In addition, the method includes generating, using the at least one processor, blur effects in the video stream using the multiple blur kernel maps.

Abstract: A display device may include a substrate including a plurality of first areas and a plurality of second areas adjacent to the first areas, a pixel defining layer, and a light blocking layer. The pixel defining layer is disposed on the substrate. The pixel defining layer defines a plurality of first openings positioned in each of the first areas and a plurality of second openings positioned in each of the second areas. The light blocking layer is disposed in each of the second areas on the substrate and surrounds the second openings in the plan view. An area of each of the second openings may be larger than an area of each of the first openings in the plan view.

Abstract: Provided is a display device including a substrate including a first display area, a second display area including transmission areas, and a non-display area, main pixel electrodes above the first display area, auxiliary pixel electrodes above the second display area, and a shield layer between the substrate and the auxiliary pixel electrodes, including a first metal layer, and at least one reflection reduction layer overlapping the first metal layer, and defining an opening overlapping the transmission areas.

Abstract: A method includes receiving a selection of a selected zoom area on an input image frame displayed on a user interface; determining one or more candidate zoom previews proximate to the selected zoom area using a saliency detecting algorithm; and displaying the one or more candidate zoom previews on the user interface adjacent to the selected zoom area.

Abstract: A display device includes: a first substrate including a first region, a second region adjacent to the first region, and a third region adjacent to the second region; a plurality of emission layers on the first substrate; a first anti-reflection layer on the plurality of emission layers, and to transmit a first colored light; a second anti-reflection layer on the plurality of emission layers, and to transmit a second colored light and a third colored light that are different from the first colored light; and a second substrate on the first anti-reflection layer and the second anti-reflection layer.

Abstract: A method includes processing, using at least one processor of an electronic device, multiple reference images of a scene using a first convolutional neural network (CNN) to generate a confidence map and a disparity map. The method also includes generating, using the at least one processor, an initial Bokeh image based on the disparity map and the reference images using a depth-of-field (DoF) renderer. The method further includes refining, using the at least one processor, the initial Bokeh image using a second CNN to generate a refined Bokeh image, where the second CNN uses the confidence map, the disparity map, and the reference images to generate the refined Bokeh image.

Abstract: An electronic device includes a display, a camera, and a processing device. The processing device is configured to determine whether (i) a user's face or eyes are within the camera's field of view or (ii) a gaze of the user is directed towards the display. The processing device is also configured, in response to determining that (i) the user's face or eyes are not within the camera's field of view or (ii) the gaze of the user is not directed towards the display, to modify a user interface button presented on the display. The user interface button may represent a shutter button configured to cause the camera or another camera of the electronic device to capture one or more images.

Abstract: A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Abstract: A method includes obtaining, using at least one processor of an electronic device, multiple calibration parameters associated with multiple sensors of a selected mobile device. The method also includes obtaining, using the at least one processor, an identification of multiple imaging tasks. The method further includes obtaining, using the at least one processor, multiple synthetically-generated scene images. In addition, the method includes generating, using the at least one processor, multiple training images and corresponding meta information based on the calibration parameters, the identification of the imaging tasks, and the scene images. The training images and corresponding meta information are generated concurrently, different ones of the training images correspond to different ones of the sensors, and different pieces of the meta information correspond to different ones of the imaging tasks.

Abstract: A method includes obtaining, using first and second image sensors of an electronic device, first and second images, respectively, of a scene. The method also includes obtaining, using an image depth sensor of the electronic device, a third image and a first depth map of the scene, the first depth map having a resolution lower than a resolution of the first and second images. The method further includes undistorting the first and second images using the third image and the first depth map. The method also includes rectifying the first and second images using the third image and the first depth map. The method further includes generating a disparity map using the first and second images that have been undistorted and rectified. In addition, the method includes generating a second depth map using the disparity map and the first depth map, where the second depth map has a resolution that is higher than the resolution of the first depth map.

Abstract: A method includes obtaining at least one image and a ground truth map associated with the at least one image. The method also includes generating multiple optical maps using multiple algorithms and the at least one image. The method further includes, for each algorithm, identifying at least one score for the algorithm using one or more of the optical maps generated using the algorithm and the ground truth map. The ground truth map identifies one or more boundaries associated with one or more foreground objects in the at least one image. The scores identify how well the optical maps generated using the algorithms separate the one or more foreground objects from a background in the at least one image.

Abstract: An electronic device, a method, and computer readable medium for operating an electronic device are disclosed. The method includes receiving data about a state of the electronic device from one or more sensors of the electronic device. The method also includes determining whether to modify a user interface button displayed on a display of the electronic device based on the received state data and parameters of a neural network. The method further includes modifying display of the user interface button on the display of the electronic device based on determining to modify. The method additionally includes providing, to the neural network, feedback data indicating whether the user interface button was triggered within a predetermined time period after modifying the user interface button.

Abstract: A mobile hyperspectral camera system is described. The mobile hyperspectral camera system comprises a mobile host device comprising a processor and a display: a plurality of cameras, coupled to the processor, configured to capture images in distinct spectral bands; and a hyperspectral flash array, coupled to the processor, configured to provide illumination to the distinct spectral bands. A method of implementing a mobile hyperspectral camera system is also described.

Abstract: A method includes obtaining a first image of a scene using a first image sensor of an electronic device and a second image of the scene using a second image sensor of the electronic device. The method also includes generating a first feature map from the first image and a second feature map from the second image. The method further includes generating a third feature map based on the first feature map, the second feature map, and an asymmetric search window. The method additionally includes generating a depth map by restoring spatial resolution to the third feature map.

Abstract: A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.

Abstract: A method includes receiving a selection of a selected zoom area on an input image frame displayed on a user interface; determining one or more candidate zoom previews proximate to the selected zoom area using a saliency detecting algorithm; and displaying the one or more candidate zoom previews on the user interface adjacent to the selected zoom area.

Abstract: A method includes processing, using at least one processor of an electronic device, each of multiple images using a photometric augmentation engine, where the photometric augmentation engine performs one or more photometric augmentation operations. The method also includes applying, using the at least one processor, multiple layers of a convolutional neural network to each of the images, where each layer generates a corresponding feature map. The method further includes processing, using the at least one processor, at least one of the feature maps using at least one feature augmentation engine between consecutive layers of the multiple layers, where the at least one feature augmentation engine performs one or more feature augmentation operations.

Abstract: A method includes obtaining at least three input image frames of a scene captured using at least three imaging sensors. The input image frames include a reference image frame and multiple non-reference image frames. The method also includes generating multiple disparity maps using the input image frames. Each disparity map is associated with the reference image frame and a different non-reference image frame. The method further includes generating multiple confidence maps using the input image frames. Each confidence map identifies weights associated with one of the disparity maps. In addition, the method includes generating a depth map of the scene using the disparity maps and the confidence maps. The imaging sensors are arranged to define multiple baseline directions, where each baseline direction extends between the imaging sensor used to capture the reference image frame and the imaging sensor used to capture a different non-reference image frame.

Abstract: An embodiment of this disclosure provides a wearable device. The wearable device includes a memory configured to store a plurality of content for display, a transceiver configured to receive the plurality of content from a connected device, a display configured to display the plurality of content, and a processor coupled to the memory, the display, and the transceiver. The processor is configured to control the display to display at least some of the plurality of content in a spatially arranged format. The displayed content is on the display at a display position. The plurality of content, when shown on the connected device, is not in the spatially arranged format. The processor is also configured to receive movement information based on a movement of the wearable device. The processor is also configured to adjust the display position of the displayed content according to the movement information of the wearable device.

Abstract: A method includes obtaining, using first and second image sensors of an electronic device, first and second images, respectively, of a scene. The method also includes obtaining, using an image depth sensor of the electronic device, a third image and a first depth map of the scene, the first depth map having a resolution lower than a resolution of the first and second images. The method further includes undistorting the first and second images using the third image and the first depth map. The method also includes rectifying the first and second images using the third image and the first depth map. The method further includes generating a disparity map using the first and second images that have been undistorted and rectified. In addition, the method includes generating a second depth map using the disparity map and the first depth map, where the second depth map has a resolution that is higher than the resolution of the first depth map.