Image processing method and apparatus, training method and apparatus of machine learning model, and storage medium
AssigneeCanon Kabushiki Kaisha
InventorNorihito Hiasa
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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.
Claims12
An image processing method comprising:
- 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,
- wherein 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, and
- wherein the machine learning model generates the first map based on the captured image and a second map representing the luminance saturation area of the captured image.
An image processing method comprising:
- a first step of acquiring a captured image;
- a second step of generating a first map based on the captured image using a machine learning model;
- and
- a third step of generating a model output based on the captured image using the machine learning model,
- wherein 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, and
- wherein the model output is a recognition label or a spatially arranged signal sequence corresponding to the captured image.
An image processing apparatus comprising at least one processor or circuit configured to execute a plurality of tasks including:
- an acquiring task configured to acquire a captured image;
- and
- a generating task configured to generate a first map based on the captured image using a machine learning model,
- wherein 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, and
- wherein the machine learning model generates the first map based on the captured image and a second map representing the luminance saturation area of the captured image.
An image processing system comprising:
- an image processing apparatus;
- and
- a system controller is communicable with the image processing apparatus,
- wherein the image processing apparatus includes at least one processor or circuit configured to execute a plurality of tasks including:
- an acquiring task configured to acquire a captured image;
- and
- a generating task configured to generate a first map based on the captured image using a machine learning model,
- wherein 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,
- wherein the system controller sends a request to perform processing on the captured image, and
- wherein the image processing apparatus performs processing on the captured image based on the request.
An image processing apparatus comprising at least one processor or circuit configured to execute a plurality of tasks including:
- an acquiring task configured to acquire a captured image,
- a first generating task configured to generate a first map based on the captured image using a machine learning model;
- and,
- a second generating task configured to generate a model output based on the captured image using the machine learning model,
- wherein 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, and
- wherein the model output is a recognition label or a spatially arranged signal sequence corresponding to the captured image.
Dependencies are inferred from the claim text.