18283007. Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium simplified abstract (BOE Technology Group Co., Ltd.)
Contents
- 1 Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium
Organization Name
BOE Technology Group Co., Ltd.
Inventor(s)
Pablo Navarrete Michelini of Beijing (CN)
Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium - A simplified explanation of the abstract
This abstract first appeared for US patent application 18283007 titled 'Image Processing Method, Electronic Device and Non-Transient Computer Readable Medium
Simplified Explanation
The abstract describes an image processing method that involves using a trained multi-scale detail enhancement model to enhance details in an input image. The method includes performing multi-scale decomposition on the input image to obtain a base layer image and at least one detail layer image, acquiring residual features corresponding to different feature maps, obtaining base layer output image and detail layer output images, and performing image fusion to obtain an output image.
- Trained multi-scale detail enhancement model used for detail enhancement
- Multi-scale decomposition performed on input image to obtain base layer and detail layer images
- Acquisition of residual features corresponding to different feature maps
- Obtaining base layer output image and detail layer output images
- Image fusion performed to obtain final output image
Potential Applications
This technology can be applied in various fields such as photography, medical imaging, satellite imaging, and video processing.
Problems Solved
This technology helps in enhancing the details in images, improving image quality, and making images more visually appealing.
Benefits
The benefits of this technology include improved image quality, enhanced details, and better visualization of images.
Potential Commercial Applications
Potential commercial applications of this technology include image editing software, medical imaging devices, surveillance systems, and satellite imaging technology.
Possible Prior Art
One possible prior art for this technology could be existing image processing algorithms that focus on detail enhancement and image fusion techniques.
What are the specific features of the trained multi-scale detail enhancement model used in this method?
The specific features of the trained multi-scale detail enhancement model used in this method include its ability to enhance details at different scales, its effectiveness in preserving image quality, and its adaptability to various types of images.
How does the image fusion process in this method contribute to the overall enhancement of the output image?
The image fusion process in this method combines the enhanced base layer image with the detail layer output images to create a final output image that maintains both the overall structure of the image and the enhanced details, resulting in a visually appealing and high-quality image.
Original Abstract Submitted
An image processing method, comprising: by using a trained multi-scale detail enhancement model, performing detail enhancement on an input image to be processed; wherein multi-scale decomposition is performed on the input image to obtain a base layer image and at least one detail layer image; a first residual feature corresponding to a first feature map is acquired, and a second residual feature corresponding to a second feature map of each detail layer image is acquired; a base layer output image is obtained according to the first residual feature, each second residual feature and the first feature map, and a detail layer output image corresponding to the detail layer image is obtained according to the first residual feature, each second residual feature and the second feature map; and image fusion is performed on the base layer output image and each detail layer output image to obtain an output image.
