Samsung Electronics Co., Ltd.

Irina Kim

Seongwook Song

Youngil Seo

Yunseok Choi

Yun Seok Choi

Woong-Hee Lee

Young Il Seo

Jeongguk Lee

Hansol Lee

Byeoung-su Kim

Eun Doo Heo

Dongpan Lim

Eundoo Heo

Dong Pan Lim

Jeong Guk Lee

An image processing apparatus includes a pre-processor configured to receive, from an image sensor, input image data including a pattern corresponding to a filter array of the image sensor, and generate reconstructed image data by reconstructing the input image data based on phase information corresponding to each of a plurality of pixels of the image sensor, and a neural network processor configured to generate output image data based on the input image data and the reconstructed image data.

Image processing apparatus including pre-processor and neural network processor and operating method thereof

An image processing device includes: an image sensor configured to generate first image data by using a color filter array; and processing circuitry configured to select a processing mode from a plurality of processing modes for the first image data, the selecting being based on information about the first image data; generate second image data by reconstructing the first image data using a neural network processor based on the processing mode; and generate third image data by post-processing the second image data apart from the neural network processor based on the processing mode.

Image processing device including neural network processor and operating method thereof

An image processing apparatus includes a camera configured to generate first image data, a pre-processor configured to generate second image data based on performing a pre-processing operation on the first image data, a neural network processor configured to generate third image data based on performing an image processing operation on the second image data based on using a neural network model trained to perform one or more particular image processing operations, and a main processor configured to generate corrected image data based on performing a post-processing operation on the third image data.

Image processing apparatus including neural network processor and method of operating the same

An image processing apparatus includes; an image sensor including pixels that generate first image data, and an image processing system. The image processing system includes; a neural network processor that performs a cluster-level bad pixel correction operation on the first image data based on first coordinate information associated with first-type bad pixel clusters to generate second image data, and a main processor that performs a post-processing operation on the second image data to generate third image data.

Image processing apparatus including neural network processor and method of operation

An electronic device is described, that includes a processing logic configured to receive input image data and generate output image data having a different format from the input image data using an artificial neural network model. The artificial neural network model includes a plurality of encoding layer units, including a plurality of layers located at a plurality of levels, respectively. The artificial neural network model also includes a plurality of decoding layer units including a plurality of layers and configured to form skip connections with the plurality of encoding layer units at the same levels. A first encoding layer unit of a first level receives a first input feature map and outputs a first output feature map. A first output feature map is based on the first input feature map, to a subsequent encoding layer unit and a decoding layer unit at the first level.

Artificial neural network model and electronic device including the same

An image processing device including a memory; and at least one image signal processor configured to: generate, using a first neural network, a feature value indicating whether to correct a global pixel value sensed during a unit frame interval, and generate a feature signal including the feature value; generate an image signal by merging the global pixel value with the feature signal; split a pixel value included in the image signal into a first sub-pixel value and a second sub-pixel value, split a frame feature signal included in the image signal into a first sub-feature value corresponding to the first sub-pixel value and a second sub-feature value corresponding to the second sub-pixel value, and generate a first sub-image signal including the first sub-pixel value and the first sub-feature value, and a second sub-image signal including the second sub-pixel value and the second sub-feature value; and sequentially correct the first sub-image signal and the second sub-image signal using a second neural network.

Image signal processor and electronic device and electronic system including the same

A method of controlling a stereo convergence and an image processor using the method are provided. The method includes: detecting objects from a stereo image; grouping the detected objects into at least one or more objects according to setup specification information; and moving at least one of left and right eye images of a stereo image of the grouping or a setup area including the grouping in a horizontal or vertical direction or in the horizontal and vertical directions.

Method of controlling stereo convergence and stereo image processor using the same

At least one example embodiment discloses a method of detecting an object in an image. The method includes receiving an image, generating first images for performing a first classification operation based on the received image, reviewing first-image features of the first images using a first feature extraction method with first-type features, first classifying at least some of the first images as second images, the classified first images having first-image features matching the first-type features, reviewing second-image features of the second images using a second feature extraction method with second-type features, second classifying at least some of the second images as third images, the classified second images having second-image features matching the second-type features and detecting an object in the received image based on results of the first and second classifying.

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Suwon-si, South Korea

Irina Kim