Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure relate to a stand break detection system. In some embodiments, the stand break detection system includes a sensor and a data processing system. The sensor may measure a movement of drill pipe as it is tripped out of a wellbore. The processor may execute instructions stored in a memory to receive measured movement data from the sensor and identify a stand break associated with one or more drill pipes using the measured movement data.

Abstract: 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.

Abstract: 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 netwo

Abstract: 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.

Abstract: 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 netwo

Abstract: 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.

Abstract: Aspects of the present disclosure relate to a stand break detection system. In some embodiments, the stand break detection system includes a sensor and a data processing system. The sensor may measure a movement of drill pipe as it is tripped out of a wellbore. The processor may execute instructions stored in a memory to receive measured movement data from the sensor and identify a stand break associated with one or more drill pipes using the measured movement data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: At least one example embodiment discloses a method of detecting an object in an image.

Abstract: 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.