Samsung Electronics Co., Ltd.

Kaimo Lin

Youngjun Yoo

Chenchi Luo

Hamid Rahim Sheikh

Yingmao Li

David D Liu

George Q Chen

Michael Oliver Polley

Ibrahim Pekkucuksen

Gyeongmin Choe

An apparatus for processing image data may include: a memory storing instructions; and a processor configured to execute the instructions to: extract a target image patch including a target object, from a captured image; obtain a plurality of landmark features from the target image patch; align the plurality of landmark features of the target image patch with a plurality of reference landmark features in a template image patch including the same target object; and when the plurality of landmark features are aligned with the plurality of reference landmark features, transfer texture details of the target object in the template image patch to the target object in the target image patch.

Method and apparatus for enhancing texture details of images

An apparatus includes at least one processing device configured to obtain an input image and determine a cumulative distribution function (CDF) histogram from a luminance or luma (Y) channel of the input image. The at least one processing device is also configured to determine an entry CDF histogram in a CDF histogram lookup table (LUT) closest to the determined CDF histogram. The at least one processing device is further configured to apply a Y channel global tone mapping (GTM) curve to the input image based on one or more parameters assigned to the entry CDF histogram from the CDF histogram LUT.

Global tone mapping with contrast enhancement and chroma boost

A method includes obtaining a Bayer input image. The method also includes generating, using at least one processing device of an electronic device, multiple YUV image frames based on the Bayer input image using non-linear scaling, where the YUV image frames are associated with different exposure settings. The method further includes generating, using the at least one processing device of the electronic device, a fused image based on the YUV image frames. In addition, the method includes applying, using the at least one processing device of the electronic device, global tone-mapping to the fused image in order to generate a tone-mapped fused image, where the global tone-mapping is based on a first cubic spline curve.

Image generation using non-linear scaling and tone-mapping based on cubic spline curves

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.

System and method for generating bokeh image for DSLR quality depth-of-field rendering and refinement and training method for the same

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.

Array-based depth estimation

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.

System and method for synthetic depth-of-field effect rendering for videos

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.

Asymmetric normalized correlation layer for deep neural network feature matching

The present disclosure describes a system and method for background identification for videos with large foreground objects. The method includes dividing a video clip into a multiple short video clips, each of which overlaps with neighboring short video clips of the multiple short video clips, detecting, in each short video clip, multiple background motion candidates, linking the background motion candidates between the neighboring short video clips to form multiple linked groups of background motion candidates, and identifying a linked group of background motion candidates that is most dominant among the multiple linked groups of background motion candidates.

Background identification for videos with large foreground objects

A method of capturing a photo using a mobile device is described. The method comprises detecting a signature motion of the mobile device; capturing video using a camera of the mobile device upon detecting the signature motion; and automatically identifying a start frame and an end frame of the captured images using neural networks. A mobile device for capturing a photo is also described.

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Richardson, TX, United States of America

Kaimo Lin