Abstract: A training apparatus is provided. The training apparatus acquires a mosaic image, generates a demosaic image by subjecting the mosaic image to a demosaicing process in which a neural network is used, and detects a low-image-quality portion in the demosaic image as a detected region. The training apparatus acquires a training image including a region having a hue similar to a hue of the detected region, and incrementally trains the neural network using the training image.

Abstract: An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

Abstract: Molten iron is obtained with a low electric power consumption rate by efficiently and reliably preheating a cold iron source by ascertaining the preheating state of the cold iron source. A method of producing molten iron in an electric furnace having a preheating chamber and a melting chamber includes: a cold iron source feeding step; a cold iron source preheating step having a preheating confirmation step; a supply step; and a melting step.

Abstract: An object is to obtain a trained model whose robustness is high in a case of learning a network in an image signal processing system. For teacher images consisting an acquired teacher image group, a distribution characteristic of at least one of three attributes of color is analyzed and based on analysis results, a teacher image group uniform in the distribution characteristic is generated. Learning of a network is performed by generating a data set including a set of a teacher image and a pupil image from a new teacher image group thus obtained.

Abstract: The information processing apparatus obtains a distribution characteristic representing an attribute of a color included in an acquired first image group and determines the number of times of learning based on the distribution characteristic. The information processing apparatus generates a data set consisting of a set of the first image group and a second image group corresponding to the first image group and by using the generated data set, generates a learning model by performing learning using a network based on the determined number of times of learning.

Abstract: An image processing apparatus includes a setting unit, a small region setting unit, a determination unit, a search unit, a noise reduction unit, and a synthesis unit. The setting unit sets a target patch in an input image. The small region setting unit sets a small region near the target patch. The determination unit determines a number of similar patches to be detected as a patch similar to the target patch based on a noise amount in the small region. The search unit searches the input image for the similar patch based on the number of similar patches. The noise reduction unit configured to perform noise reduction processing with respect to the target patch using the target patch and the similar patch. The synthesis unit generates an image by synthesizing a plurality of patch groups subjected to the noise reduction processing.

Abstract: An information processing apparatus includes an identification unit configured to identify a partial region of an input image, and a processing unit configured to perform image processing for reducing degradation of the input image on the input image by inference using a neural network. The processing unit is configured to change the image processing between the partial region and another region.

Abstract: An apparatus acquires an image group including a plurality of first images, generates a plurality of second images by adding predetermined noise to each of the plurality of first images and applying a digital gain, generates learning data including respective pairs of the plurality of first images and the corresponding plurality of second images, and performs learning using a neural network and the learning data. The noise added when the plurality of second images is generated is noise reproducing a histogram containing discrete distribution decreases.

Abstract: The present disclosure provides a technology capable of favorably using a vehicle driven by a driver or an autonomous vehicle as a delivery vehicle to deliver goods. An information processing apparatus includes a controller including at least one processor configured to execute assigning either a first vehicle that is a vehicle driven by a driver or a second vehicle that is an autonomous vehicle as a delivery vehicle to deliver goods to a user. When a predetermined event other than the delivery of the goods to the user is requested to be performed at a delivery destination, the controller selects the first vehicle as the delivery vehicle.

Abstract: An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

Abstract: An information processing device determines, based on location information of a plurality of delivery destinations for package delivery: one or a plurality of first delivery destinations for package delivery by a vehicle; and one or a plurality of second delivery destinations for package delivery by a drone mounted on the vehicle. In addition, the information processing device determines: a travel route including a route for the vehicle to perform package delivery to the one or plurality of first delivery destinations; a first point on the travel route for the drone to start flying from the vehicle to the one or plurality of second delivery destinations; and a second point on the travel route for the drone to return to the vehicle.

Abstract: An image processing apparatus is disclosed. The image processing apparatus acquires a mosaic image. The image processing apparatus generates a first demosaic image by subjecting the mosaic image to a first demosaicing process in which a neural network is used, and generates a second demosaic image by subjecting the mosaic image to a second demosaicing process that is different from the first demosaicing process. The image processing apparatus generates a composite image in which the first demosaic image and the second demosaic image are combined.

Abstract: A training apparatus is provided. The training apparatus acquires a mosaic image, generates a demosaic image by subjecting the mosaic image to a demosaicing process in which a neural network is used, and detects a low-image-quality portion in the demosaic image as a detected region. The training apparatus acquires a training image including a region having a hue similar to a hue of the detected region, and incrementally trains the neural network using the training image.

Abstract: There is provided an apparatus including a first correction unit configured to acquire a first corrected pixel using the covariance matrix, a determination unit configured to determine whether the first corrected pixel is abnormal, a second correction unit configured to acquire a second corrected pixel by performing a second correction on a pixel at a position of the first corrected pixel determined to be abnormal.

Abstract: An image processing apparatus includes a setting unit, a small region setting unit, a determination unit, a search unit, a noise reduction unit, and a synthesis unit. The setting unit sets a target patch in an input image. The small region setting unit sets a small region near the target patch. The determination unit determines a number of similar patches to be detected as a patch similar to the target patch based on a noise amount in the small region. The search unit searches the input image for the similar patch based on the number of similar patches. The noise reduction unit configured to perform noise reduction processing with respect to the target patch using the target patch and the similar patch. The synthesis unit generates an image by synthesizing a plurality of patch groups subjected to the noise reduction processing.

Abstract: An image processing apparatus includes a setting unit, a small region setting unit, a determination unit, a search unit, a noise reduction unit, and a synthesis unit. The setting unit sets a target patch in an input image. The small region setting unit sets a small region near the target patch. The determination unit determines a number of similar patches to be detected as a patch similar to the target patch based on a noise amount in the small region. The search unit searches the input image for the similar patch based on the number of similar patches. The noise reduction unit configured to perform noise reduction processing with respect to the target patch using the target patch and the similar patch. The synthesis unit generates an image by synthesizing a plurality of patch groups subjected to the noise reduction processing.

Abstract: An extreme ultraviolet light generation apparatus may include a chamber device, a concentrating mirror, a central gas supply port configured to supply gas along a focal line passing through a first focal point and a second focal point from the center side of the reflection surface, and a first peripheral gas supply port disposed at a peripheral portion of the reflection surface and configured to supply gas in a direction from the outer side of the reflection surface toward the inner side of the reflection surface. The first peripheral gas supply port may supply gas, when viewed along the focal line, in an inclined direction inclined to a tangential direction side of the peripheral portion at the peripheral portion where the first peripheral gas supply port is located with respect to a first straight line passing through the first peripheral gas supply port and the focal line.

Abstract: The information processing apparatus obtains a distribution characteristic representing an attribute of a color included in an acquired first image group and determines the number of times of learning based on the distribution characteristic. The information processing apparatus generates a data set consisting of a set of the first image group and a second image group corresponding to the first image group and by using the generated data set, generates a learning model by performing learning using a network based on the determined number of times of learning.

Abstract: An object is to obtain a trained model whose robustness is high in a case of learning a network in an image signal processing system. For teacher images consisting an acquired teacher image group, a distribution characteristic of at least one of three attributes of color is analyzed and based on analysis results, a teacher image group uniform in the distribution characteristic is generated. Learning of a network is performed by generating a data set including a set of a teacher image and a pupil image from a new teacher image group thus obtained.

Abstract: An extreme ultraviolet light generation apparatus may include a chamber device, a concentrating mirror, a central gas supply port configured to supply gas along a focal line passing through a first focal point and a second focal point from the center side of the reflection surface, and a first peripheral gas supply port disposed at a peripheral portion of the reflection surface and configured to supply gas in a direction from the outer side of the reflection surface toward the inner side of the reflection surface. The first peripheral gas supply port may supply gas, when viewed along the focal line, in an inclined direction inclined to a tangential direction side of the peripheral portion at the peripheral portion where the first peripheral gas supply port is located with respect to a first straight line passing through the first peripheral gas supply port and the focal line.