Abstract: Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

Abstract: A processor-implemented method with image restoration includes obtaining an input image, generating a correction image by converting the input image based on a conversion parameter that converts a pixel value distribution of the input image, generating an output image by inputting the correction image to a quantized image restoration model, and generating an enhanced image by inversely converting the output image based on an inverse conversion parameter corresponding to the conversion parameter.

Abstract: A processor-implemented method includes obtaining a color filter array (CFA) input image, obtaining pattern information corresponding to a CFA, preprocessing the input image based on the pattern information, generating an inferred image by inputting the preprocessed input image to an artificial neural network (ANN) model, and generating an output image by selecting, for each pixel, from either one of the preprocessed input image and the inferred image, based on the pattern information.

Abstract: An example electronic device includes a battery, a coil antenna that transmits power of the battery, a shield disposed on a lower surface of the coil antenna, and a plurality of magnet modules formed to surround at least a portion of a side surface of the coil antenna. Each of the plurality of magnet modules includes an inner part that faces toward the side surface of the coil antenna, an outer part that faces toward an opposite side to the coil antenna, and a non-magnetized area disposed between the inner part and the outer part. The inner part includes a first inner area and a second inner area over the first inner area, and the outer part includes a first outer area and a second outer area over the first outer area. The first inner area and the second outer area are magnetized with a first polarity, and the second inner area and the first outer area are magnetized with a second polarity opposite to the first polarity.

Abstract: A system includes: an image sensor configured to acquire an image; an image processor configured to generate a quantized image based on the acquired image using a trained quantization filter; and an output interface configured to output the quantized image.

Abstract: An electronic device includes: one or more processors configured to: generate a transformed frequency image by transforming an input image into a frequency domain; obtain a frequency filter corresponding to the input image by inputting the generated transformed frequency image to a filter generation neural network; and generate an output image in which a noise distribution of the input image is normalized by applying the obtained frequency filter to the generated transformed frequency image.

Abstract: A method and apparatus for processing an image based on partial images. The method includes extracting a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN), updating a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region by inputting the extracted feature into the RNN, and generating an image processing result for the input image frame based on the updated hidden state.

Abstract: A system includes: an image sensor configured to acquire an image; an image processor configured to generate a quantized image based on the acquired image using a trained quantization filter; and an output interface configured to output the quantized image.

Abstract: A method, implemented by a processor, of correcting lighting of an image includes inputting an input image to a first neural network and generating predicted lighting data corresponding to lighting of the input image and embedding data corresponding to a feature of the input image, inputting the generated predicted lighting data, the generated embedding data, and sensor data to a second neural network and generating a lighting weight corresponding to the input image, and generating correction lighting data for the input image by applying the generated lighting weight to preset basis lighting data corresponding to the input image.

Abstract: A method, implemented by a processor, of correcting lighting of an image includes inputting an input image to a first neural network and generating predicted lighting data corresponding to lighting of the input image and embedding data corresponding to a feature of the input image, inputting the generated predicted lighting data, the generated embedding data, and sensor data to a second neural network and generating a lighting weight corresponding to the input image, and generating correction lighting data for the input image by applying the generated lighting weight to preset basis lighting data corresponding to the input image.

Abstract: A neural network-based image processing method and apparatus are provided. A method includes receiving input image data comprising original image data, when a current image processing mode is an independent processing mode, generating first intermediate image data by executing a main neural network based on the original image data and by not executing an auxiliary neural network based on the original image data, when the current image processing mode is in a cooperative processing mode, generating second intermediate image data by determining an auxiliary parameter by executing the auxiliary neural network based on the original image data and by executing the main neural network based on the original image data and based on the auxiliary parameter, and generating output image data by operating an image signal processing (ISP) block based the first intermediate image data or the second intermediate image data according to the current image processing mode.

Abstract: An image restoration method and apparatus are provided. The image restoration method includes determining auxiliary data corresponding to a plurality of filter kernels by filtering target data with the plurality of filter kernels, determining new input data by combining the auxiliary data with at least some input data of layers of a neural network-based image restoration model, generating, based on the new input data, a restored image of the input image by executing the neural network-based image restoration model, wherein the filter kernels are not part of the neural network-based image restoration model.

Abstract: Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

Abstract: Disclosed is a method and apparatus for face detection using an adaptive threshold. The method includes determining a detection box in an input image, calculating a confidence score indicating whether an object in the detection box corresponds to a face, setting an adaptive threshold based on a size of the detection box, and determining whether the object in the detection box corresponds to a face based on comparing the confidence score to the adaptive threshold.

Abstract: An apparatus and method with image processing for sparse dual-pixel image data are included. In one general aspect, an apparatus includes a processor configured to generate a first interpolated image by performing interpolation on a first image and generate a second interpolated image by performing interpolation on a second image, wherein the first image and the second image comprise pixel values obtained by dual-pixels of an image sensor, and generate a disparity map based on the first interpolated image and the second interpolated image.

Abstract: Disclosed is a method and apparatus for face detection using an adaptive threshold. The method includes determining a detection box in an input image, calculating a confidence score indicating whether an object in the detection box corresponds to a face, setting an adaptive threshold based on a size of the detection box, and determining whether the object in the detection box corresponds to a face based on comparing the confidence score to the adaptive threshold.

Abstract: An example electronic device includes a battery, a coil antenna that transmits power of the battery, a shield disposed on a lower surface of the coil antenna, and a plurality of magnet modules formed to surround at least a portion of a side surface of the coil antenna. Each of the plurality of magnet modules includes an inner part that faces toward the side surface of the coil antenna, an outer part that faces toward an opposite side to the coil antenna, and a non-magnetized area disposed between the inner part and the outer part. The inner part includes a first inner area and a second inner area over the first inner area, and the outer part includes a first outer area and a second outer area over the first outer area. The first inner area and the second outer area are magnetized with a first polarity, and the second inner area and the first outer area are magnetized with a second polarity opposite to the first polarity.

Abstract: Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.

Abstract: Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

Abstract: Disclosed are an image data processing method and a sensor device performing the same. The sensor device includes an image sensor configured to acquire image data, an image buffer configured to store the image data, and an image processor configured to generate image-processed data by applying a filter corresponding to a storage pattern of the image buffer to the image data stored in the image buffer.