Abstract
The structure tensor is a powerful tool describing the local intensity structure of an image or image sequence. In this paper we give a model for the noise distribution of the components of the tensor. In order to do so we have also investigated some properties of the gamma distribution. We show that, given an input image corrupted with Gaussian noise, the noise in the structure tensor can be modeled well by gamma distributions. We apply our model to automatic contrast enhancement of images taken under poor illumination. We show how our noise model can be used for automatic parameter selection in the filtering process, giving powerful results without the need for cumbersome parameter tuning.
| Original language | English |
|---|---|
| Title of host publication | Pattern Recognition (ICPR), 2016 23rd International Conference on |
| Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
| Pages | 763-768 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5090-4847-2 |
| DOIs | |
| Publication status | Published - 2017 Apr 24 |
| Event | 2016 23rd International Conference on Pattern Recognition (ICPR 2016) - Cancún Center, Cancún, Mexico Duration: 2016 Dec 4 → 2016 Dec 8 http://www.icpr2016.org/site/ |
Conference
| Conference | 2016 23rd International Conference on Pattern Recognition (ICPR 2016) |
|---|---|
| Abbreviated title | ICPR 2016 |
| Country/Territory | Mexico |
| City | Cancún |
| Period | 2016/12/04 → 2016/12/08 |
| Internet address |
Subject classification (UKÄ)
- Probability Theory and Statistics
- Signal Processing
- Computer Vision and Robotics (Autonomous Systems)
Free keywords
- Enhancement
- restoration and filtering
- Signal, image and video processing
- Computational photography