A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

An apparatus for super resolution imaging includes a convolutional neural network (104) to receive a low resolution frame (102) and generate a high resolution illuminance component frame. The apparatus also includes a hardware scaler (106) to receive the low resolution frame (102) and generate a second high resolution chrominance component frame. The apparatus further includes a combiner (108) to combine the high resolution illuminance component frame and the high resolution chrominance component frame to generate a high resolution frame (110).

Provided is an electronic apparatus configured to provide an image based on artificial intelligence (AI), the electronic apparatus including a processor configured to execute one or more instructions stored in the electronic apparatus to obtain a first image by AI-downscaling an original image by a downscaling neural network, obtain first image data by encoding the first image, based on a display apparatus not supporting an AI upscaling function, obtain a second image by decoding the first image data, obtain a third image by AI-upscaling the second image by an upscaling neural network, and provide, to the display apparatus, second image data obtained by encoding the third image.

Techniques are described for enhancing the quality of three-dimensional (3D) anatomy scan images using deep learning. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component that receives a scan image generated from 3D scan data relative to a first axis of a 3D volume, and an enhancement component that applies an enhancement model to the scan image to generate an enhanced scan image having a higher resolution relative to the scan image. The enhancement model comprises a deep learning neural network model trained on training image pairs respectively comprising a low-resolution scan image and a corresponding high-resolution scan image respectively generated relative to a second axis of the 3D volume.

An information processing apparatus includes a processor configured to: obtain a video and an instruction to generate a still image from the video, the video being a video in which a work target is photographed, the work target being a target on which to work; generate the still image in response to the instruction, the still image being cut from the video including the work target; specify the work target in the video, position information, and a superimposition area by using the still image, the position information describing a position of the work target, the superimposition area being an area in which an image is superimposed, the image being obtained by using the position of the work target as a reference; receive instruction information indicating an instruction for work on the work target; and superimpose and display an instruction image in the superimposition area in the video, the instruction image being an image according to the instruction information.

An information processing apparatus includes a processor configured to: obtain a video and an instruction to generate a still image from the video, the video being a video in which a work target is photographed, the work target being a target on which to work; generate the still image in response to the instruction, the still image being cut from the video including the work target; specify the work target in the video, position information, and a superimposition area by using the still image, the position information describing a position of the work target, the superimposition area being an area in which an image is superimposed, the image being obtained by using the position of the work target as a reference; receive instruction information indicating an instruction for work on the work target; and superimpose and display an instruction image in the superimposition area in the video, the instruction image being an image according to the instruction information.

The present disclosure describes devices and methods for generating RGB images from Bayer filter images using adaptive sub-pixel spatiotemporal interpolation. An electronic device includes a processor configured to estimate green values at red and blue pixel locations of an input Bayer frame based on green values at green pixel locations of the input Bayer frame and a kernel for green pixels, generate a green channel of a joint demosaiced-warped output RGB pixel from the input Bayer frame based on the green values at the green pixel locations, the kernel for green pixels, and an alignment vector map, and generate red and blue channels of the joint demosaiced-warped output RGB pixel from the input Bayer frame based on the estimated green values at the red and blue pixel locations, kernels for red and blue pixels, and the alignment vector map.

Systems and methods for identifying inclined regions are provided. In one aspect, a method is provided that includes receiving shadow data for at least one first ground object in a first region, wherein each first ground object is depicted in one overhead image of the first region, wherein the shadow data comprises a length of the respective first ground object as identified from the respective overhead image; receiving shadow data for at least one second comparable ground object in a second region, wherein each second ground object is depicted in one overhead image of the second region, wherein the shadow data comprises a length of the respective second ground object as identified from the respective overhead image; calculating a statistical measure describing the variability of the shadow lengths between objects in the first region and the second region; comparing the statistical measure to a predetermined threshold; and based on the comparison, identifying that the first region is inclined relative to the second region.

A transformer may transform image data from a first color pattern to a second color pattern. The transforming of image data may be applied to image data received from a memory storing an array of intensities corresponding to a first color pattern of a first color filter array (CFA) of an image sensor to a second color pattern. The second color pattern may be a color pattern of a size smaller that the first CFA. Remosaicing may be applied to the second color pattern to obtain image data organized in a Bayer color pattern. The transforming may be configured to operate on data from an image sensor to obtain different zoom levels not available without applying a digital zoom algorithm that involve upscaling, which reduces the image quality of the image data.