Authors:
Simon Reich
1
;
Florentin Wörgötter
1
and
Babette Dellen
2
Affiliations:
1
Georg-August-Universität Göttingen, Germany
;
2
Hochschule Koblenz, Germany
Keyword(s):
Edge-Preserving, Denoising, Real-Time.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
Even in todays world, where augmented reality glasses and 3d sensors become rapidly less expensive and
widely more used, the most important sensor remains the 2d RGB camera. Every camera is an optical device
and prone to sensor noise, especially in dark environments or environments with extreme high dynamic range.
The here introduced filter removes a wide variation of noise, for example Gaussian noise and salt-and-pepper
noise, but preserves edges. Due to the highly parallel structure of the method, the implementation on a GPU
runs in real-time, allowing us to process standard images within tens of milliseconds. The filter is first tested
on 2d image data and based on the Berkeley Image Dataset and Coco Dataset we outperform other standard
methods. Afterwards, we show a generalization to arbitrary dimensions using noisy low level sensor data. As
a result the filter can be used not only for image enhancement, but also for noise reduction on sensors like
acceleremoters, gyroscopes, or
GPS-trackers, which are widely used in robotic applications.
(More)