View source for Samsung electronics co., ltd. (20240193728). METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL simplified abstract

=METHOD AND ELECTRONIC DEVICE FOR TRAINING IMAGE PROCESSING MODEL AND METHOD AND ELECTRONIC DEVICE FOR PROCESSING IMAGES USING IMAGE PROCESSING MODEL=


==Organization Name==

[[:Category:samsung electronics co., ltd.|samsung electronics co., ltd.]]


[[Category:samsung electronics co., ltd.]]

==Inventor(s)==

[[:Category:Chao Zhang of Beijing (CN)|Chao Zhang of Beijing (CN)]][[Category:Chao Zhang of Beijing (CN)]]

[[:Category:Qiang Wang of Beijing (CN)|Qiang Wang of Beijing (CN)]][[Category:Qiang Wang of Beijing (CN)]]

[[:Category:Jingu Heo of Suwon-si (KR)|Jingu Heo of Suwon-si (KR)]][[Category:Jingu Heo of Suwon-si (KR)]]

[[:Category:Dong Kyung Nam of Suwon-si (KR)|Dong Kyung Nam of Suwon-si (KR)]][[Category:Dong Kyung Nam of Suwon-si (KR)]]

[[:Category:Yasi Wang of Beijing (CN)|Yasi Wang of Beijing (CN)]][[Category:Yasi Wang of Beijing (CN)]]

==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 20240193728 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 input images, each containing multiple low-resolution images from different viewpoints. Features are extracted from each low-resolution image, fused together to create a fusion residual feature, and used to generate a high-resolution image corresponding to the input image group.

* The method involves processing a group of input images, each containing multiple low-resolution images from different viewpoints.
* Features are extracted from each low-resolution image in the input image group.
* These features are fused together to create a fusion residual feature.
* The fusion residual feature is used to generate a high-resolution image corresponding to the input image group.

'''Potential Applications:'''
This technology can be used in various fields such as medical imaging, satellite imaging, surveillance systems, and digital photography to enhance image quality and detail.

'''Problems Solved:'''
This technology addresses the challenge of generating high-resolution images from low-resolution inputs, improving image quality and detail for various applications.

'''Benefits:'''
The method can significantly enhance image quality, provide more detailed information, and improve the overall visual experience in applications such as medical imaging, surveillance, and photography.

'''Commercial Applications:'''
The technology can be applied in industries such as healthcare, defense, and entertainment for enhancing image quality, improving analysis accuracy, and enhancing user experience.

'''Prior Art:'''
Prior research in image processing and super-resolution techniques may provide insights into similar methods and technologies.

'''Frequently Updated Research:'''
Stay updated on the latest advancements in image processing, super-resolution techniques, and applications in various industries to leverage the full potential of this technology.

'''Questions about Image Processing:'''
1. How does this image processing method improve the quality of low-resolution images?
2. What are the potential applications of this technology in different industries?


==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.



[[Category:G06T3/4053]]
[[Category:G06T7/20]]
[[Category:G06V10/24]]
[[Category:G06V10/44]]
[[Category:G06V10/80]]
[[Category:CPC_G06T3/4053]]