Google llc (20240135492). IMAGE SUPER-RESOLUTION NEURAL NETWORKS simplified abstract

IMAGE SUPER-RESOLUTION NEURAL NETWORKS

Organization Name

google llc

Inventor(s)

Cristina Nader Vasconcelos of Montreal (CA)

Ahmet Cengiz Oztireli of Zurich (CH)

Andrea Tagliasacchi of Toronto (CA)

Kevin Jordan Swersky of Toronto (CA)

Mark Jeffrey Matthews of Los Angeles CA (US)

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 20240135492 titled 'IMAGE SUPER-RESOLUTION NEURAL NETWORKS

Simplified Explanation

The abstract 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. Here is a simplified explanation of the patent application:

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

Potential Applications

The technology can be applied in image processing, medical imaging, satellite imaging, surveillance systems, and video enhancement.

Problems Solved

The technology addresses the issue of enhancing image resolution without losing quality, improving image clarity and detail for various applications.

Benefits

The benefits of this technology include improved image quality, enhanced visual perception, better analysis of images, and increased accuracy in image recognition tasks.

Potential Commercial Applications

The technology can be utilized in industries such as healthcare, security, entertainment, and remote sensing for commercial applications like medical diagnostics, security surveillance, video production, and environmental monitoring.

Possible Prior Art

One possible prior art could be the use of traditional image upscaling techniques that may not provide the same level of quality and detail as the super-resolution neural network described in the patent application.

How does this technology compare to existing image upscaling methods?

The technology described in the patent application utilizes a super-resolution neural network to generate higher resolution images. It would be interesting to know how it compares in terms of image quality, processing speed, and resource efficiency compared to traditional upscaling techniques.

What are the potential limitations or challenges of implementing this technology in real-world applications?

While the patent application outlines a method for processing images using a super-resolution neural network, there may be challenges in scaling this technology for real-time applications, optimizing it for different types of images, and ensuring compatibility with existing systems.

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.