Increasing the Resolution of Space Imagery.
Analyzing data from space missions can be challenging.
The data is limited by the precision of onboard instruments and is a product of the harsh environment of outer space.
As such, observations can be riddled with anomalies and background noise.
This makes scientific work cumbersome and time consuming.
Machine learning models can help with this.
New machine learning models are helping researchers to eliminate the background noise.
When eliminating the background noise faint features that otherwise would stay hidden come to light.
This can open up our understanding of the universe around us.
AerospaceAI’s Machine Learning model has shown to reduce background noise by 94%.
It is able to increase the resolution of the image by up to 4 times,
and able to bring to light objects that are 10 times fainter in the original image,
allowing researchers to make new discoveries.
Use Cases
Cleaning up the images from space observatories.
Images that are taken from Earth are spekkled with noise and challenged by parameters such as weather, light pollution and passing satellites.
Cleaning up those images allows space observatories to increase the reliability of their observations.
Increasing the resolution of deep space data.
Space missions are characterized by years of planning and execution.
Increasing the precision of data taken during these missions can help achieve their goals and give a new lease of life to decades of old data from previous missions.
Highlighting faint objects in space.
With special pattern recognition faint objects can not only be brought to the front of the image, they can also be compared with known data.
This allows them to be identified even if they don’t show up clearly on the enhanced images.