18602325. INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD simplified abstract (CANON KABUSHIKI KAISHA)
Contents
- 1 INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD - 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 Machine Learning Model for Noise Reduction in Moving Images
- 1.13 Original Abstract Submitted
INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD
Organization Name
Inventor(s)
NAOKI Kakinuma of Kanagawa (JP)
INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18602325 titled 'INFORMATION PROCESSING APPARATUS, IMAGE PROCESSING APPARATUS, AND INFORMATION PROCESSING METHOD
Simplified Explanation
An information processing apparatus is disclosed that trains a machine learning model to reduce noise in a moving image. The apparatus performs two training sessions, one to reduce noise and the other to reduce image quality degradation caused by variation between frames in the image.
Key Features and Innovation
- Information processing apparatus trains machine learning model for noise reduction in moving images
- First training session reduces noise in the image
- Second training session reduces degradation of image quality caused by variation between frames
- Trained model outputs processed image for noise reduction in target frames
Potential Applications
This technology can be used in various industries such as:
- Film and video production
- Surveillance systems
- Medical imaging
- Satellite imaging
- Automotive industry for camera systems
Problems Solved
- Reducing noise in moving images
- Minimizing degradation of image quality due to frame variation
- Enhancing image processing capabilities
Benefits
- Improved image quality in moving images
- Enhanced noise reduction capabilities
- Increased accuracy in image processing tasks
Commercial Applications
- "Machine Learning Model for Noise Reduction in Moving Images" can be utilized in:
- Film production studios
- Security and surveillance companies
- Medical imaging facilities
- Satellite imaging companies
- Automotive industry for camera systems
Prior Art
Readers interested in exploring prior art related to this technology can start by researching machine learning models for noise reduction in images and image processing techniques for moving images.
Frequently Updated Research
Stay updated on the latest advancements in machine learning models for noise reduction in moving images and image processing technologies to enhance your understanding of this field.
Questions about Machine Learning Model for Noise Reduction in Moving Images
What are the key benefits of using machine learning models for noise reduction in moving images?
Using machine learning models for noise reduction in moving images can significantly improve image quality by reducing unwanted noise and enhancing overall clarity.
How does the second training session in the information processing apparatus help in reducing image quality degradation caused by frame variation?
The second training session in the apparatus focuses on minimizing the impact of variation between frames in the image, ensuring a more consistent and high-quality output.
Original Abstract Submitted
An information processing apparatus that trains a machine learning model for reducing noise in a moving image is disclosed. The information processing apparatus performs a first training in which a first training dataset is applied to the machine learning model and a second training in which a second training dataset is applied to the machine learning model after the first training has ended. The trained machine learning model outputs an image as a processing result for a target frame for noise reduction from an input image consists of a plurality of frames including the target frame. The first training is to reduce noise, and the second training is to reduce degradation of image quality caused by variation between the plurality of frames.
