Removal of adherent noises from image sequences by spatio-temporal image processing
Abstract
This paper describes a method for removing adherent noises from image sequences. In outdoor environments, it is often the case that scenes taken by a camera are deteriorated because of adherent noises such as waterdrops on the surface of the lens-protecting glass of the camera. To solve this problem, our method takes advantage of image sequences captured with a moving camera. The method makes a spatio-temporal image to extract the regions of adherent noises by examining differences of track slopes in cross section images between adherent noises and other objects. Finally, regions of noises are eliminated by replacing with image data corresponding to object regions. Experimental results show the effectiveness of our method.
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The spatio-temporal algorithm requires collecting and post-processing a sequence of image frames over time, which makes it not appropriate for real-time automotive applications.
“This algorithm requires collecting and post-processing a sequence of image frames over time which makes it not appropriate for real-time automotive applications.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [33]
Yamashita et al.'s spatio-temporal algorithm detection results were comparable to those conducted by a human observer.
“Detection results with this algorithm were comparable to those conducted by a human observer.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [33]
Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
Nonlinear total variation based noise removal algorithms
Rudin, L., Osher, S., & Fatemi, E. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 17,089 · see citing works
Bertalmı́o, M., Sapiro, G., Caselles, V., & Ballester, C. (2000) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/344779.344972 · Cited by 3,747 · see citing works
Texture synthesis by non-parametric sampling
Efros, A. A., & Leung, T. K. (1999) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1999.790383 · Cited by 3,058 · see citing works
Camera calibration with distortion models and accuracy evaluation
Weng, J., Cohen, P. R., & Herniou, M. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.159901 · Cited by 1,767 · see citing works
Simultaneous structure and texture image inpainting
Bertalmı́o, M., Vese, L. A., Sapiro, G., & Osher, S. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.815261 · Cited by 1,083 · see citing works
Navier-stokes, fluid dynamics, and image and video inpainting
Bertalmı́o, M., Bertozzi, A. L., & Sapiro, G. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990497 · Cited by 1,063 · see citing works
Space-Time Completion of Video
Wexler, Y., Shechtman, E., & Irani, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.60 · Cited by 681 · see citing works
Level lines based disocclusion
Masnou, S., & Morel, J. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1998.999016 · Cited by 538 · see citing works
Detection and removal of rain from videos
Garg, K., & Nayar, S. K. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315077 · Cited by 531 · see citing works
Simultaneous structure and texture image inpainting
Bertalmı́o, M., Vese, L. A., Sapiro, G., et al. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211536 · Cited by 251 · see citing works
Interpolation of missing data in image sequences
Kokaram, A. C., Morris, R. D., Fitzgerald, W. J., et al. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.469932 · Cited by 215 · see citing works
Full-Frame Video Stabilization
Matsushita, Y., Ofek, E., Tang, X., et al. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.166 · Cited by 137 · see citing works
Detection and removal of line scratches in motion picture films
Joyeux, L., Buisson, O., Besserer, B., et al. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1999.786991 · Cited by 101 · see citing works
Real-time snowfall noise elimination
Hase, H., Miyake, K., & Yoneda, M. (1999) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1999.822927 · Cited by 55 · see citing works
Video Completion for Perspective Camera Under Constrained Motion
Shen, Y., Lu, F., Cao, X., et al. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.1169 · Cited by 54 · see citing works
Yamashita, A., Fukuchi, I., & Kaneko, T. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354639 · Cited by 33 · see citing works
Self-citationA virtual wiper - restoration of deteriorated images by using multiple cameras
Yamashita, A., Kuramoto, M., Kaneko, T., & Miura, K. T. (2004) · Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) · doi:10.1109/iros.2003.1249637 · Cited by 32 · see citing works
Self-citationRemoval of Adherent Waterdrops from Images Acquired with a Stereo Camera System
Tanaka, Y. (2006) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e89-d.7.2021 · Cited by 31 · see citing works
Inpainting from multiple views
Kang, S. H., Chan, T. F.-C., & Soatto, S. (2003) · International Symposium on 3D Data Processing, Visualization and Transmission · doi:10.1109/tdpvt.2002.1024127 · Cited by 26 · see citing works
Removal of adherent noises from images of dynamic scenes by using a pan-tilt camera
Yamashita, A., Harada, T., Kaneko, T., et al. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389391 · Cited by 25 · see citing works
Self-citationA virtual wiper-restoration of deteriorated images by using a pan-tilt camera
Yamashita, A., Kaneko, T., & Miura, K. T. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1302464 · Cited by 17 · see citing works
Self-citationVirtual wiper — removal of adherent noises from images of dynamic scenes by using a pan–tilt camera
Yamashita, A., Harada, T., Kaneko, T., et al. (2005) · Advanced Robotics · doi:10.1163/1568553053583643 · Cited by 16 · see citing works
Self-citationTanaka, Y., Yamashita, A., Kaneko, T., et al. (2005) · Cited by 3 · see citing works
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Video-based raindrop detection for improved image registration
Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops · doi:10.1109/iccvw.2009.5457650 · Cited by 126 · see citing works
Detecting unfocused raindrops: In-vehicle multipurpose cameras
Cord, A., & Gimonet, N. (2014) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2013.2287451 · Cited by 35 · see citing works
Raindrop detection and removal using salient visual features
Wu, Q., Zhang, W., & Vijaya Kumar, B. V. K. (2012) · 2012 19th IEEE International Conference on Image Processing · doi:10.1109/icip.2012.6467016 · Cited by 27 · see citing works
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 21 · see citing works
“[33] Decompose the image into structure and texture images.”
BackgroundMethodInfluentialVideo-based water drop detection and removal method for a moving vehicle
Liao, H.-C., Wang, D.-Y., Yang, C.-L., & Shin, J. (2013) · Information Technology Journal · doi:10.3923/itj.2013.569.583 · Cited by 9 · see citing works
- Authors
- Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T.
- Affiliations
- California Institute of Technology (US)
- Shizuoka University (JP)
- Venue
- 2008 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2008-05
- Pages
- 2386-2391
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2146032989
- Cited by
- 21
- References
- 24
- Citations by year
- 2021: 1 · 2020: 1 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 1.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Stabilization
- Keywords
- image sequences, moving camera, cross-section images
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