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GoogleGranted patentAI & softwareInterest 6/10

Google patents image filter that removes haze and noise unevenly

This patent describes a method for cleaning up photos by using a machine-learned model to estimate how much haze or noise is present at different parts of an image. It creates a map of these imperfections, refines it with a filter, and then applies the corrections to produce a clearer, color-adjusted final picture.

Why it mattersThis improves the automatic cleanup features in photo editing apps, making images look sharper and more vibrant without manual effort.

In the filing’s own words

Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

What exactly they claim (claim 1)

1. A computer-implemented method of noise reduction, the computer-implemented method comprising: determining, by a computing system comprising one or more processors, an image type associated with an input image; selecting, by the computing system, a respective size for each of a plurality of feature windows based on the image type associated with the input image; generating, by the computing system, based on the input image, a plurality of feature vectors associated with the plurality of feature windows for a point among a plurality of points in the input image; providing, by the computing system, the plurality of feature vectors as an input to one or more machine-learned models to generate a noise magnitude map associated with an amount of noise at the point; generating, by the computing system, based on the noise magnitude map and a guided filter, a refined noise magnitude map; generating, by the computing system, a denoised image based on applying the refined noise magnitude map to the input image; and generating, by the computing system, a color corrected denoised image based on performing one or more color correction operations on the denoised image including replacing, by the computing system, low-frequency color space components of the denoised image with low-frequency color space components of the input image.

Facts

Company
Google (Google LLC)
Official title
Spatially varying reduction of haze in images
Patent number
US12762662B2
Application number
18469144
Filed
Sep 18, 2023
Granted
Oct 13, 2026
Inventors
3
Claims
20

Classified under

G06T 5/70 Physics & computingG06T 3/40 Physics & computingG06T 5/20 Physics & computingG06V 10/56 Physics & computingG06V 10/764 Physics & computingG06V 10/7715 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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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.