Back to results
US 2024/0404251 A1Published application
Image processing apparatus, operation method therefor, inference apparatus, and learning apparatus
AssigneeFUJIFILM Corporation
InventorShumpei KAMON
Life of this patent
Estimating the term…
Abstract
A learning input image is input to a first sub-model to extract a first feature map, and a first output image is output based on the first feature map. The first feature map is input to a second sub-model to extract a second feature map, and a second output image having a higher resolution than the first output image is output. In response to an inference input image being input to a trained learned model, the first output image as an inference result image is output.
Claims18
An image processing apparatus comprising:
- a processor configured to:
- output a first output image based on a first feature map extracted by inputting a learning input image to a first sub-model in a learning model including the first sub-model and a second sub-model;
- output a second output image having a higher resolution than the first output image, based on a second feature map extracted by inputting the first feature map to the second sub-model;
- calculate an evaluation result by using the second output image;
- update the learning model by using the evaluation result to set the learning model as a learned model including a first sub-learned model that is the first sub-model that has performed learning and a second sub-learned model that is the second sub-model that has performed learning;
- and
- output the first output image as an inference result image based on the first feature map extracted by inputting an inference input image to the first sub-learned model in the learned model.
4 more elements
An operation method for an image processing apparatus, the operation method comprising steps of:
- outputting a first output image based on a first feature map extracted by inputting a learning input image to a first sub-model in a learning model including the first sub-model and a second sub-model;
- outputting a second output image having a higher resolution than the first output image, based on a second feature map extracted by inputting the first feature map to the second sub-model;
- calculating an evaluation result by using the second output image;
- updating the learning model by using the evaluation result to set the learning model as a learned model including a first sub-learned model that is the first sub-model that has performed learning and a second sub-learned model that is the second sub-model that has performed learning;
- and
- outputting the first output image as an inference result image based on the first feature map extracted by inputting an inference input image to the first sub-learned model in the learned model.
3 more elements
An inference apparatus comprising:
- a processor configured to output a first output image as an inference result image, based on a first feature map extracted by inputting an inference input image to a first sub-learned model in a learned model including the first sub-learned model and a second sub-learned model, wherein
- the learned model is generated by setting, in a learning model including a first sub-model and a second sub-model, the first sub-model as the first sub-learned model and the second sub-model as the second sub-learned model, and
- the learning model outputs a first output image based on the first feature map extracted based on a learning input image input to the first sub-model, outputs a second output image having a higher resolution than the first output image, based on a second feature map extracted based on the first feature map input to the second sub-model, and is updated by using an evaluation result calculated using the second output image to perform learning.
A learning apparatus comprising:
- a processor configured to:
- output a first output image based on a first feature map extracted by inputting a learning input image to a first sub-model in a learning model including the first sub-model and a second sub-model;
- output a second output image having a higher resolution than the first output image, based on a second feature map extracted by inputting the first feature map to the second sub-model;
- calculate an evaluation result by using the second output image;
- and
- update the learning model by using the evaluation result to perform learning, wherein
- the resolution of the second output image is lower than a resolution of the learning input image.
4 more elements
Dependencies are inferred from the claim text.