View source for 18379519. IMAGE SUPER-RESOLUTION NEURAL NETWORKS simplified abstract (GOOGLE LLC)

=IMAGE SUPER-RESOLUTION NEURAL NETWORKS=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Cristina Nader Vasconcelos of Montreal (CA)|Cristina Nader Vasconcelos of Montreal (CA)]][[Category:Cristina Nader Vasconcelos of Montreal (CA)]]

[[:Category:Ahmet Cengiz Oztireli of Zurich (CH)|Ahmet Cengiz Oztireli of Zurich (CH)]][[Category:Ahmet Cengiz Oztireli of Zurich (CH)]]

[[:Category:Andrea Tagliasacchi of Toronto (CA)|Andrea Tagliasacchi of Toronto (CA)]][[Category:Andrea Tagliasacchi of Toronto (CA)]]

[[:Category:Kevin Jordan Swersky of Toronto (CA)|Kevin Jordan Swersky of Toronto (CA)]][[Category:Kevin Jordan Swersky of Toronto (CA)]]

[[:Category:Mark Jeffrey Matthews of Los Angeles CA (US)|Mark Jeffrey Matthews of Los Angeles CA (US)]][[Category:Mark Jeffrey Matthews of Los Angeles CA (US)]]

[[:Category:Milad Olia Hashemi of San Francisco CA (US)|Milad Olia Hashemi of San Francisco CA (US)]][[Category:Milad Olia Hashemi of San Francisco CA (US)]]

==IMAGE SUPER-RESOLUTION NEURAL NETWORKS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18379519 titled 'IMAGE SUPER-RESOLUTION NEURAL NETWORKS

==Simplified Explanation==

The patent application describes methods, systems, and apparatus for processing an input image using a super-resolution neural network to generate a higher resolution version of the input image. 

* Processing input image using an encoder subnetwork to generate a feature map
* Generating an updated feature map by applying convolutional filters parametrized by a hyper neural network
* Processing the updated feature map using a projection subnetwork to generate the up-sampled image

== Potential Applications ==
The technology can be used in image processing applications such as enhancing the resolution of images captured by cameras or improving the quality of medical imaging.

== Problems Solved ==
This technology addresses the issue of low-resolution images by using neural networks to generate higher resolution versions of the input images.

== Benefits ==
The benefits of this technology include improved image quality, enhanced details in images, and the ability to upscale images without losing quality.

== Potential Commercial Applications ==
Commercial applications of this technology could include software for enhancing image quality in photography, medical imaging devices, and video processing tools.

== Possible Prior Art ==
One possible prior art for this technology could be traditional image upscaling techniques that do not utilize neural networks for generating high-resolution images.

== Unanswered Questions ==

=== How does this technology compare to traditional image upscaling methods? ===
This article does not provide a direct comparison between this technology and traditional image upscaling methods. 

=== What are the computational requirements for implementing this super-resolution neural network? ===
The article does not delve into the computational requirements needed to implement this super-resolution neural network.


==Original Abstract Submitted==

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input image using a super-resolution neural network to generate an up-sampled image that is a higher resolution version of the input image. In one aspect, a method comprises: processing the input image using an encoder subnetwork of the super-resolution neural network to generate a feature map; generating an updated feature map, comprising, for each spatial position in the updated feature map: applying a convolutional filter to the feature map to generate a plurality of features corresponding to the spatial position in the updated feature map, wherein the convolutional filter is parametrized by a set of convolutional filter parameters that are generated by processing data representing the spatial position using a hyper neural network; and processing the updated feature map using a projection subnetwork of the super-resolution neural network to generate the up-sampled image.



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