Upscaling Images with AI and Sub-Pixel Information
This patent describes how artificial intelligence, specifically neural networks, can make low-resolution images look sharper by cleverly guessing new pixel colors based on tiny details between existing pixels.
Original patent title: “Upsampling an image using one or more neural networks”
This patent describes how artificial intelligence, specifically neural networks, can make low-resolution images look sharper by cleverly guessing new pixel colors based on tiny details between existing pixels. Granted in 2026.
Coverage
What does this patent actually cover?
The patent describes using one or more neural networks to create higher-resolution images from lower-resolution ones. It achieves this by determining 'pixel weights' based on 'sub-pixel offset values.' This means the system looks at the tiny spaces *between* existing pixels in the original image. It then uses these calculated weights to intelligently fill in the new pixels needed for the larger, sharper image. For example, if you have a blurry photo, this system could use AI to add new pixels, making edges look smoother and details clearer by considering where colors should blend at a microscopic level.
The gap
What does this patent NOT cover?
- Does not cover image upsampling methods that rely solely on traditional techniques like bilinear or bicubic interpolation without neural networks.
- Does not cover neural network-based upsampling that does not determine pixel weights based on sub-pixel offset values, as specified in the abstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →.
- Does not cover image generation techniques that create entirely new images rather than upscaling existing ones.
- Does not cover image enhancement methods that do not involve increasing resolution, such as noise reduction or color correction, unless they also involve upsampling.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → appears to be in how the neural network specifically uses 'sub-pixel offset values' to determine 'pixel weights.' This suggests a fine-grained approach to interpolating new pixel data, going beyond simply looking at the immediate surrounding pixels to reconstruct details more accurately.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
AI image upscalers
Video streaming services (e.g., Netflix, YouTube)
Digital photography software (e.g., Adobe Photoshop)
Medical imaging systems
Security camera systems
Video game upscaling technologies (e.g., DLSS, FSR)
Why it matters
The bigger picture
High-quality images are crucial for everything from medical imaging to entertainment. AI upsampling can make old photos look new, improve video streaming quality, and enhance details in security footage. This technology helps make digital content look better on high-resolution screens and can reduce the storage or bandwidth needed for high-quality visual data.
Filed
February 10, 2021
Granted
September 15, 2026
Market context
Who's building on this
Companies in this space
Companies like NVIDIA, Adobe, Google, and various startups are actively developing and integrating AI upscaling technologies into their products. These range from graphics cards with dedicated upscaling hardware to cloud-based image processing services and consumer applications for photo and video enhancement. The original assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more → is unknown, but the field is highly active.
Market impact
AI-powered image upscaling has significantly improved the quality of digital media, enabling clearer images and videos from lower-resolution sources. This has impacted streaming services, digital photography, and gaming, allowing content to look better on high-definition displays and potentially reducing bandwidth needs for transmitting high-quality visuals. It enables new applications for older, lower-resolution content.
Claim 1 — Plain English
What this patent covers
The patent describes using one or more neural networks to create higher-resolution images from lower-resolution ones. It achieves this by determining 'pixel weights' based on 'sub-pixel offset values.' This means the system looks at the tiny spaces *between* existing pixels in the original image. It then uses these calculated weights to intelligently fill in the new pixels needed for the larger, sharper image. For example, if you have a blurry photo, this system could use AI to add new pixels, making edges look smoother and details clearer by considering where colors should blend at a microscopic level.
The clever bit
The novelty appears to be in how the neural network specifically uses 'sub-pixel offset values' to determine 'pixel weights.' This suggests a fine-grained approach to interpolating new pixel data, going beyond simply looking at the immediate surrounding pixels to reconstruct details more accurately.
What it does not cover
- Does not cover image upsampling methods that rely solely on traditional techniques like bilinear or bicubic interpolation without neural networks.
- Does not cover neural network-based upsampling that does not determine pixel weights based on sub-pixel offset values, as specified in the abstract.
- Does not cover image generation techniques that create entirely new images rather than upscaling existing ones.
- Does not cover image enhancement methods that do not involve increasing resolution, such as noise reduction or color correction, unless they also involve upsampling.
Patent timeline
Application submitted to the patent office
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
0/20
Narrow claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$19K – $61K
Midpoint $38K · 14.3 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
Concepts involved
Cite this patent
(2026). Upscaling Images with AI and Sub-Pixel Information (U.S. Patent No. 12,737,843). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12737843/upsampling-an-image-using-one-or-more-neural-networks
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Upscaling Images with AI and Sub-Pixel Information cover?
This patent describes how artificial intelligence, specifically neural networks, can make low-resolution images look sharper by cleverly guessing new pixel colors based on tiny details between existing pixels.
When does this patent expire?
This patent is expected to expire on September 15, 2046, when the invention enters the public domain.
What problem does this patent solve?
High-quality images are crucial for everything from medical imaging to entertainment. AI upsampling can make old photos look new, improve video streaming quality, and enhance details in security footage. This technology helps make digital content look better on high-resolution screens and can reduce the storage or bandwidth needed for high-quality visual data.
What does this patent NOT cover?
Does not cover image upsampling methods that rely solely on traditional techniques like bilinear or bicubic interpolation without neural networks.
Patent monitoring