View source for 17211330. Super Resolution for Satellite Images simplified abstract (Microsoft Technology Licensing, LLC)

=Super Resolution for Satellite Images=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Peder A. Olsen of Redmond WA (US)|Peder A. Olsen of Redmond WA (US)]][[Category:Peder A. Olsen of Redmond WA (US)]]

[[:Category:Ranveer Chandra of Kirkland WA (US)|Ranveer Chandra of Kirkland WA (US)]][[Category:Ranveer Chandra of Kirkland WA (US)]]

[[:Category:Olaoluwa Adigun of Los Angeles CA (US)|Olaoluwa Adigun of Los Angeles CA (US)]][[Category:Olaoluwa Adigun of Los Angeles CA (US)]]

==Super Resolution for Satellite Images - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17211330 titled 'Super Resolution for Satellite Images

==Simplified Explanation==
The patent application describes systems and methods for generating high-resolution images from low-resolution images using machine learning and super resolution models.

* Low-resolution images undergo sensor transformation with a machine learning model.
* Low-resolution images are combined with land structure features and/or prior high-resolution images to create an augmented input.
* A super resolution model processes the augmented input to generate an initial predicted high-resolution image.
* The initial predicted high-resolution image can be stacked with other predicted images and processed by another super resolution model to generate a final high-resolution image.

==Key Features and Innovation==
* Utilization of machine learning models and super resolution models in a series of processes.
* Sensor transformation of low-resolution images.
* Combination of low-resolution images with land structure features and/or prior high-resolution images.
* Stacking of predicted high-resolution images to generate a final image.

==Potential Applications==
The technology can be used in various fields such as satellite imaging, medical imaging, surveillance systems, and photography.

==Problems Solved==
The technology addresses the challenge of generating high-resolution images from low-resolution inputs with enhanced accuracy and detail.

==Benefits==
* Improved image quality and resolution.
* Enhanced accuracy in image generation.
* Versatile applications across different industries.

==Commercial Applications==
Title: Enhanced Image Resolution Technology for Various Industries
The technology can be commercialized in satellite imaging companies, medical imaging facilities, surveillance system providers, and photography studios. It can improve the quality and accuracy of images in these industries, leading to better decision-making and analysis.

==Prior Art==
Readers can explore prior research on machine learning models for image enhancement and super resolution techniques in the field of computer vision.

==Frequently Updated Research==
Researchers are continually developing new machine learning algorithms and super resolution models to further enhance image quality and resolution. Stay updated on the latest advancements in this field to leverage cutting-edge technology for image processing.

==Questions about Image Resolution Technology==
===How does machine learning contribute to enhancing image resolution?===
Machine learning models analyze and process low-resolution images to generate high-resolution outputs by learning patterns and features from training data.

===What are the potential drawbacks of using super resolution models in image enhancement?===
Super resolution models may introduce artifacts or distortions in the generated high-resolution images if not properly trained or optimized. Regular updates and improvements are necessary to mitigate these issues.


==Original Abstract Submitted==

Systems and methods for generating predicted high-resolution images from low-resolution images. To generate the predicted high-resolution images, the present technology may utilize machine learning models and super resolution models in a series of processes. For instance, the low-resolution images may undergo a sensor transformation based on processing by a machine learning model. The low-resolution images may also be combined with land structure features and/or prior high-resolution images to form an augmented input that is processed by a super resolution model to generate an initial predicted high-resolution image. The predicted initial high-resolution image may be combined or stacked with other predicted high-resolution images to form a stacked image. That stacked image may then be processed by another super resolution model to generate a final predicted high-resolution image.



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