Abstract: An image processing method includes acquiring a captured image obtained by image capturing using an optical system, acquiring correction strength for a sharpening process applied to a partial area in the captured image, and applying the sharpening process to the partial area based on a first optical characteristic and the correction strength, wherein the correction strength is acquired based on a second optical characteristic, wherein the first optical characteristic is an optical characteristic of the optical system in a case where the optical system is defocused by a first defocus amount, wherein the second optical characteristic is an optical characteristic of the optical system in a case where the optical system is defocused by a second defocus amount, and wherein the second defocus amount is smaller than the first defocus amount.

Abstract: An image processing method includes obtaining a captured image by image capturing using an optical apparatus, and obtaining resolution performance information about a resolution performance of the optical apparatus, and generating an output image by reducing a sampling pitch of the captured image based on the captured image and the resolution performance information, wherein the information indicating the resolution performance is a map, and each pixel of the map indicates the resolution performance of a corresponding pixel of the captured image.

Abstract: A method includes sharpening a first image to generate a second image, generating a first weight map based on the first image, generating a second weight map based on the second image, and generating a third image based on the first image, the second image, the first weight map, and the second weight map, wherein each of the first weight map and the second weight map indicate a weight of one of the first image and the second image in the generation of the third image.

Abstract: An image processing method includes a first step of acquiring input data including a captured image and optical system information relating to a state of an optical system used for capturing the captured image and a second step of inputting the input data to a machine learning model and of generating an estimated image acquired by sharpening the captured image or by reshaping blurs included in the captured image.

Abstract: An image processing method includes a first step of acquiring input data including a captured image and optical system information relating to a state of an optical system used for capturing the captured image and a second step of inputting the input data to a machine learning model and of generating an estimated image acquired by sharpening the captured image or by reshaping blurs included in the captured image.

Abstract: An image processing method comprising steps of obtaining a first map representing a region outside a dynamic range of an input image based on a signal value in the input image and a threshold of the signal value, and inputting input data including the input image and the first map and executing a recognition task or a regression task.

Abstract: An image processing method includes generating a first image by inputting a captured image, which has been acquired by imaging using an optical system, into a machine learning model, acquiring information on optical performance of the optical system, and generating a second image based on the captured image, the first image, and first weight information. The first weight information is generated based on the information on the optical performance and information on a saturated area in the captured image.

Abstract: An image processing method includes acquiring a captured image obtained by imaging, generating a first image by correcting a blur component of the captured image, and generating a second image based on the captured image, the first image, and weight information. The weight information is generated based on (i) information on brightness of the captured image or information on a scene of the captured image and (ii) information on a saturated area in the captured image.

Abstract: An image processing method includes a first step of acquiring a captured image, and a second step of generating a first map based on the captured image using a machine learning model. The first map is a map indicating a magnitude and range of a signal value in an area where an object in a luminance saturation area in the captured image is spread by a blur generated in an imaging step of the captured image.

Abstract: An image processing method configured to correct a captured image using a neural network includes a first step of determining an inversion axis for a partial image or a filter of the neural network according to a position in the captured image of the partial image that is part of the captured image, a second step of determining a positional relationship among pixels of color components in an image input to the neural network corresponding to the inversion axis, and a third step of generating a corrected image obtained by correcting an input image by processing, using the neural network, the input image generated from the captured image based on the positional relationship.

Abstract: A method for processing an image uses a generator which is a machine learning model. The generator converts an input low resolution image into a first feature map. From the first feature map, a first intermediate image and a second intermediate image each having resolution higher than resolution of the low resolution image are generated. Based on the first intermediate image and the second intermediate image, an estimated image having higher resolution than the resolution of the low resolution image is generated.

Abstract: An image processing method includes the steps of acquiring a first image obtained by imaging an object space through a first pupil in an optical system, and a second image obtained by imaging the object space through a second pupil different from the first pupil in the optical system, and generating a blur reshaped image in which a blur caused by a defocus is reshaped based on the first image and the second image using a neural network.

Abstract: An image processing method includes acquiring input data including an input image and a noise map representing a noise amount in the input image based on an optical black area corresponding to the input image, and inputting the inputdata into a neural network to execute a task of a recognition or regression.

Abstract: An image processing method includes a first step configured to obtain a first ground truth image and a first training image, a second step configured to generate a second around truth image and a second training image by applying mutually correlated noises to the first ground truth image and the first training image, and a third step configured to make a neural network learn based on the second ground truth image and the second training image.

Abstract: An image processing method includes steps of acquiring first model output generated based on a captured image by a first machine learning model, acquiring second model output generated based on the captured image by a second machine learning model which is different from the first machine learning model, and generating an estimated image by using the first model output and the second model output, based on a comparison based on the second model output and one of the captured image and first model output.

Abstract: An image processing method includes the steps of acquiring a training image and a correct image, inputting the training image into a multilayer neural network to generate an output image, performing a gamma correction for each of the correct image and the output image and calculating an error between the correct image after the gamma correction and the output image after the gamma correction, and updating a network parameter of the neural network using the error.

Abstract: An image processing method includes a first step of acquiring input data including a captured image and optical system information relating to a state of an optical system used for capturing the captured image and a second step of inputting the input data to a machine learning model and of generating an estimated image acquired by sharpening the captured image or by reshaping blurs included in the captured image.

Abstract: An image processing method comprising steps of obtaining a first map representing a region outside a dynamic range of an input image based on a signal value in the input image and a threshold of the signal value, and inputting input data including the input image and the first map and executing a recognition task or a regression task.

Abstract: An image processing method includes a first step configured to obtain a first ground truth image and a first training image, a second step configured to generate a second around truth image and a second training image by applying mutually correlated noises to the first ground truth image and the first training image, and a third step configured to make a neural network learn based on the second ground truth image and the second training image.

Abstract: An image processing method includes acquiring input data including an input image and a noise map representing a noise amount in the input image based on an optical black area corresponding to the input image, and inputting the input data into a neural network to execute a task of a recognition or regression.