Microsoft patents image repair tool that adapts to different content types
Microsoft describes a machine-trained model that fixes damaged images by using different attention techniques for various parts of the picture. The system identifies specific types of image degradation and adjusts its processing based on the content, such as text or objects, to restore the image more effectively.
In the filing’s own words
An image restoration machine-trained model (“model”) removes degradation from an input image that is associated with one or more degradation types. The model has an encoder-decoder framework that includes encoders and decoders. Each encoder and decoder processes feature information in a channel attention operation and a spatial attention operation. The model performs the channel attention operation using at least two different channel attention techniques that consume different amounts of resources. The model performs the spatial attention operation by applying different spatial attention techniques to different types of content in the input image. The feature information passed to the decoders includes degradation information that describes the types degradation in the input image. A training system updates the parameters of the model upon the introduction of a new degradation type by fine-tuning an existing version of the model, rather than training the model from scratch.
What exactly they claim (claim 1)
1. A method for reducing degradation in an input image using an image restoration machine-trained model, comprising: receiving the input image having one or more types of degradation; transforming the input image into a degradation-reduced image using an encoder-decoder framework having a sequence of encoders, followed by a sequence of decoders, the sequence of encoders ending with a bottleneck encoder, each encoder of the sequence of encoders and each decoder of the sequence of decoders processing feature information that represents the input image by: in a channel attention operation, generating a measure of relations among feature maps of the feature information; and in a spatial attention operation, generating a measure of relations among different parts of a spatial structure of the feature information, wherein the encoder-decoder framework performs the channel attention operation using at least two different channel attention machine-trained models, and wherein the encoder-decoder framework performs the spatial attention operation using different respective kinds of spatial attention models that are applied to different parts of the input image having different content types.
Facts
- Company
- Microsoft (Microsoft Technology Licensing, LLC)
- Official title
- Content and Task-Aware Image Restoration
- Publication number
- US20260268451A1
- Application number
- 19075722
- Filed
- Mar 10, 2025
- Published
- Sep 10, 2026
- Inventors
- 5
- Claims
- 20
Classified under
G06T 5/60 Physics & computingG06V 10/764 Physics & computingG06V 10/7715 Physics & computingG06V 10/774 Physics & computingG06T 2207/20081 Physics & computingG06T 2207/20084 Physics & computingMore from Microsoft in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.