Name
Title | ||
|---|---|---|
Unsymmetrically Trimmed Mean Filter for Noise Removal of Robot Vision in Dark Environments |
Abstract | ||
|---|---|---|
Overexposure is likely to occur in the vision system of robots in dark environments, which will result in noise points in the image or video. These noise points caused by overexposure are mostly salt and pepper noise, which may disturb the regular tasks of robots significantly, e.g., target tracking or object recognition. In this paper, a novel unsymmetrically trimmed mean filter (UTMF) is proposed to restore dark-captured images corrupted by salt-and-pepper noise. Firstly, the noise points are detected according to the extreme characteristic of the salt-and-pepper noise. Then, we propose a method to check whether the local pixels around a noise point are smooth or saltant via statistics of difference. Different filtering strategies are designed for smooth and saltant conditions, respectively. Extensive experiments are implemented with standard images to test the proposed method with various noise ratios from 30% to 70%, and the results verify that our method outperforms state-of-the-art methods in both peak-signal-to-noise ratio and structural similarity scores. The peak-signal-to-noise ratio of our method outperforms state-of-the-art methods by 0.1-1.2db. The images captured in dark environments with significant noise points are used to test the proposed method as well, and the results verify that our method is capable of both denoising and detail-preserving. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/RCAR.2018.8621828 | 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) |
Keywords | Field | DocType |
peak-signal-to-noise ratio,dark environments,unsymmetrically trimmed mean filter,noise removal,robot vision,salt-and-pepper noise,noise points,overexposure,local pixels,statistics-of-difference,filtering strategies,structural similarity scores | Noise reduction,Computer vision,Machine vision,Exposure,Computer science,Salt-and-pepper noise,Filter (signal processing),Truncated mean,Artificial intelligence,Pixel,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
978-1-5386-6870-2 | 0 | 0.34 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Huang | 1 | 2 | 1.38 |
| Changxin Zhou | 2 | 1 | 1.36 |
| Jianyu Yang | 3 | 429 | 78.51 |
| Zhanpeng Shao | 4 | 25 | 8.50 |
| Y. F. Li | 5 | 1128 | 105.83 |