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.

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 processor-implemented neural network layer convolution operation method includes: obtaining a first input plane of an input feature map and a first weight plane of a weight kernel; generating base planes, corresponding to an intermediate operation result of the first input plane, based on at least a portion of available weight values of the weight kernel; generating first accumulation data based on at least one plane corresponding to weight element values of the first weight plane among the first input plane and the base planes; and generating a first output plane of an output feature map based on the first accumulation data.

Abstract: Disclosed are methods and apparatuses for processing a convolution operation on a layer in a neural network. The method includes extracting a first target feature vector from a target feature map, extracting a first weight vector matched with the first target feature vector from a first-type weight element, based on matching relationships for depth-wise convolution operations between target feature vectors of the target feature map and weight vectors of the first-type weight element, generating a first intermediate feature vector by performing multiplication between the first target feature vector and the first weight vector, generating a first hidden feature vector by accumulating the first intermediate feature vector and a second intermediate feature vector generated based on a second target feature vector, and generating a first output feature vector of an output feature map based on a point-wise convolution operation between the first hidden feature vector and a second-type weight element.

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: 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 processor-implemented neural network method includes: generating a first output line of an output feature map by performing a convolution operation between a first input line group of an input feature map and weight kernels; generating a first output of an operation block including the convolution operation based on the first output line; and storing the first output in a memory in which the input feature map is stored by overwriting the first output to a memory space of the memory.

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: A processor-implemented data processing method and apparatus for a neural network is provided. The data processing method includes generating cumulative data by accumulating results of multiplication operations between at least a portion of input elements in an input plane and at least a portion of weight elements in a weight plane, and generating an output plane corresponding to an output channel among output planes of an output feature map respectively corresponding to output channels based on the generated cumulative data.

Abstract: An apparatus for providing a contents service includes a service request receiver configured to receive a contents service request from a user device, a device information manager configured to determine a type of the user device, a virtual machine generator configured to acquire template information corresponding to the determined type of the user device, and to generate a virtual machine based on the acquired template information, wherein the virtual machine generates contents in a format specific to the user device and a contents provider configured to provide the user device with the generated contents.