18509563. IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM simplified abstract (CANON KABUSHIKI KAISHA)
Contents
- 1 IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Image Processing Technology
- 1.13 Original Abstract Submitted
IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
Organization Name
Inventor(s)
IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18509563 titled 'IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM
Simplified Explanation
The patent application describes an image processing apparatus that restores a deteriorated image to its original state using a learning model trained based on the restoration accuracy and frequency characteristic of the image.
- The apparatus obtains the restoration accuracy of processing for restoring a deteriorated image.
- It also acquires the frequency characteristic of the original image.
- The first training unit trains a learning model based on the restoration accuracy and frequency characteristic.
Key Features and Innovation
- Restoration accuracy of processing for deteriorated images.
- Training a learning model based on restoration accuracy and frequency characteristic.
- Utilizing frequency characteristic of images for image restoration.
Potential Applications
The technology can be used in image editing software, medical imaging for enhancing diagnostic accuracy, and surveillance systems for improving image quality.
Problems Solved
- Restoring deteriorated images accurately.
- Enhancing image quality based on frequency characteristics.
- Training models for image restoration efficiently.
Benefits
- Improved image restoration accuracy.
- Enhanced image quality.
- Efficient training of learning models for image processing.
Commercial Applications
Title: Advanced Image Restoration Technology for Various Industries This technology can be utilized in industries such as photography, healthcare, security, and entertainment for enhancing image quality and accuracy.
Prior Art
Readers can explore prior research on image restoration techniques, frequency analysis in image processing, and machine learning models for image enhancement.
Frequently Updated Research
Stay updated on advancements in image processing algorithms, machine learning models for image restoration, and applications of frequency analysis in image enhancement.
Questions about Image Processing Technology
How does the frequency characteristic of an image impact the restoration process?
The frequency characteristic helps in identifying patterns and details in the image, which can aid in accurately restoring the deteriorated parts.
What are the potential challenges in training a learning model based on restoration accuracy and frequency characteristic?
Challenges may include optimizing the model for different types of images, ensuring generalizability, and managing computational resources efficiently.
Original Abstract Submitted
An image processing apparatus comprises a first obtaining unit configured to obtain a restoration accuracy of processing for restoring a teacher image from a deteriorated image for which deterioration has been added to the teacher image, a second obtaining unit configured to obtain a frequency characteristic of the teacher image, and a first training unit configured to perform processing for training a learning model based on the restoration accuracy and the frequency characteristic.
