An information processing apparatus includes an interpolation target color difference pixel generator that generates, on the basis of a relationship between a luminance pixel at an interpolation target pixel position that is a pixel position at which a first color difference pixel does not exist and the luminance pixel at at least one neighboring pixel position of a plurality of pixel positions near the interpolation target pixel position in image data, an interpolation target color difference pixel corresponding to the first color difference pixel at the interpolation target pixel position, in which the image data is generated on the basis of three primary color pixels that can include a value greater than a predetermined white clip value, the image data having a number of the luminance pixels larger than a number of the first color difference pixels and a number of second color difference pixels.

Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.

Disclosed are a motion compensation processing apparatus and method of medical images, in which motion of organs is corrected, the method including: acquiring the medical image and combining an organ motion component into the medical image; training at least one deep learning model based on the medical image combined with the organ motion component so that the deep learning model can remove the organ motion component; and acquiring a processing medical image, selecting a deep learning model corresponding to an organ included in the processing medical image, and removing a motion component for the organ.

Disclosed is an operating method of an electronic device which includes a processor performing machine learning of a monocular depth estimation module. The operating method includes obtaining, by the processor, a first image and a second image respectively photographed by a first camera and a second camera of different locations, inferring, by the processor, a plurality of multi-cyclic disparities by applying weights of the monocular depth estimation module to the first image plural times and calculating a plurality of multi-cyclic loss functions based on the first image, the second image, and the plurality of multi-cyclic disparities, and updating, by the processor, the weights of the monocular depth estimation module through machine learning, based on the plurality of multi-cyclic loss functions.

There is provided with an image processing apparatus. An obtaining unit obtains a first image serving as a read image of an inspection target medium having undergone printing, and a second image serving as a read image of a reference medium representing a target print result. An inspection unit inspects a defect on the inspection target medium based on the first image and the second image by performing inspection at inspection settings different between a print region and a peripheral region of the inspection target medium.

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

A method and system are presented for use in X-ray based measurements on patterned structures. The method comprises: processing data indicative of measured signals corresponding to detected radiation response of a patterned structure to incident X-ray radiation, and subtracting from said data an effective measured signals substantially free of background noise, said effective measured signals being formed of radiation components of reflected diffraction orders such that model based interpretation of the effective measured signals enables determination of one or more parameters of the patterned structure, wherein said processing comprises: analyzing the measured signals and extracting therefrom a background signal corresponding to the background noise; and applying a filtering procedure to the measured signals to subtract therefrom signal corresponding to the background signal, resulting in the effective measured signal.

This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

Systems and methods are disclosed and an example system includes a digital image receiver for receiving a digital image, and an automatic obscuration processor coupled to the image receiver and configured to determine whether the digital image includes a region that classifies as an image of a category of object and, upon a positive determination, to obscure the region and output a corresponding obscured-region digital image.

A system employs a trained model to detect artifact(s) associated with artifact type(s) appearing in a reproduction of a source image (a test image). The system determines differences between the test image and the source image and outputs probabilities that the artifact(s) in the test image are associated with each of the artifact type(s). A dataset for training the model includes: (i) a reference category including reference image(s) without any artifacts; and (ii) artifact categories, each corresponding to a respective one of the artifact types and including noised images associated with the respective artifact type. Each noised image includes one of the reference images and an artifact associated with the respective artifact type. The model is trained to detect the artifact type(s) by providing the model with the dataset and causing the model to process differences between each noised image and the reference image in the noised image.