Paper
20 May 2009 Distortion correction of optical system based on neural network and automated generation of reference points
Qiang Li, Yan-an Zeng, Zhi-xian Jiu, Kun-tao Yang
Author Affiliations +
Abstract
The distortion can be formed as the magnifying power of image changes in the field of view in a practical optical system if the field of view is large enough. Optical distortion can be corrected by aberration correction theory, as well as digital image processing technology. The theory of distortion correction by digital image processing technology is discussed, the method of getting reference points by lattice templet is introduced, and the method of generating reference points automatically is proposed. The algorithm based on BP neural network which can approximate any continuous function is used to get coordinate transform function between ideal image and distorted image. The method of bilinear gray-level interpolation is adopted because the calculation of bilinear interpolation is less than others as strict requirements on calculation in engineering application. Finally, a sample of image correction is given, and the results show that it is very fast and convenient to pick up reference point, and the precision of results is high with the method of neural network.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiang Li, Yan-an Zeng, Zhi-xian Jiu, and Kun-tao Yang "Distortion correction of optical system based on neural network and automated generation of reference points", Proc. SPIE 7283, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 72831R (20 May 2009); https://doi.org/10.1117/12.828680
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Distortion

Neural networks

Image restoration

Digital imaging

Neurons

Optical calibration

Optical networks

RELATED CONTENT


Back to Top