18532570. METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
- 1 METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL - 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
METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL
Organization Name
Inventor(s)
Dong Kyung Nam of Suwon-si (KR)
METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18532570 titled 'METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL
Simplified Explanation
The patent application describes a method for processing images using a model that involves obtaining a group of low-resolution images from different viewpoints, extracting features from these images, fusing the features to create a fusion residual feature, and generating a high-resolution image based on this fusion residual feature.
Key Features and Innovation
- Image processing method for enhancing low-resolution images from multiple viewpoints.
- Extraction of features from low-resolution images.
- Fusion of features to create a fusion residual feature.
- Generation of high-resolution images based on the fusion residual feature.
Potential Applications
This technology can be used in various fields such as:
- Medical imaging
- Satellite imaging
- Surveillance systems
- Photography
Problems Solved
- Enhancing the quality of low-resolution images.
- Providing high-resolution images from multiple viewpoints.
- Improving image processing efficiency.
Benefits
- Improved image quality.
- Enhanced detail and clarity in images.
- Increased accuracy in image processing tasks.
Commercial Applications
- "Enhanced Image Processing Method for Multiple Viewpoints": This technology can be utilized in industries such as medical imaging, surveillance, and satellite imaging to improve image quality and accuracy.
Prior Art
Prior research in image processing methods for enhancing low-resolution images and generating high-resolution images from multiple viewpoints can be found in academic journals and patents related to image processing technologies.
Frequently Updated Research
Researchers are constantly exploring new techniques and algorithms to further improve image processing methods for enhancing low-resolution images and generating high-resolution images from multiple viewpoints.
Questions about Image Processing Technology
What are the potential limitations of this image processing method?
This technology may face challenges in processing images with complex backgrounds or in situations with limited data availability.
How does this image processing method compare to existing techniques in terms of efficiency and accuracy?
This method aims to improve both the efficiency and accuracy of image processing tasks by extracting features from low-resolution images and fusing them to generate high-resolution images.
Original Abstract Submitted
Provided is an image processing method of an image processing model, the image processing method including obtaining an input image group, the input image group including a plurality of low-resolution images corresponding to a plurality of different viewpoints, respectively, obtaining a feature of low-resolution images by extracting a feature for each low-resolution image of the plurality of low-resolution images included in the input image group, obtaining a fusion residual feature by fusing the feature of low-resolution images, and obtaining a super-resolution image corresponding to the input image group based on the fusion residual feature.