A solution method for image distortion correction model based on bilinear interpolation
Li J., Su J., Zeng X.

College of Information Science & Engineering, Hunan International Economics University, Changsha 410205, China

Аннотация:
In the process of the image generation, because the imaging system itself has differences in terms of nonlinear or cameraman perspective, the generated image will face the geometric distortion. Image distortion in general is also a kind of image degradation, which needs the geometric transform to correct each pixel position of the distorted images, so as to regain the original spatial relationships between pixels and the original grey value relation, and which is also one of important steps of image processing. From the point of view of the digital image processing, the distortion correction is actually a process of image restoration for a degraded image. In image processing, in terms of the image quality improvement and correction technology, namely the image restoration, with the wide expansion of digital image distortion correction processing applied, the processing technology of the image restoration has also become a research hotspot. In view of the image distortion issue, this paper puts forward the image distortion correction algorithm based on two-step and one-dimensional linear gray level interpolation to reduce the computation complexity of the bilinear interpolation method, and divide the distorted image into multiple quadrilaterals, and the area of the quadrilateral is associated with the distortion degree of the image in the given region, and express the region distortion of each quadrilateral with the bilinear model, thus determining parameters of bilinear model according to the position of the quadrilateral vertex in the target image and the  distorted image. Experiments show that such algorithm in this paper can meet the requirements of distortion correction of most lenses, which can accurately extract the distorted edge of the image, thus making the corrected image closer to the ideal image.

Ключевые слова:
image distortion, bilinear interpolation, correction model.

Цитирование:
Li, J. A solution method for image distortion correction model based on bilinear interpolation / J. Li, J. Su, X. Zeng // Computer Optics. - 2019. - Vol. 43, Issue 1. - P. 99-104. - DOI: 10.18287/2412-6179-2019-43-1-99-104.

Литература:

  1. Geoffroy, O. Transient climate response in a two-layer energy-balance model. Part I: Analytical solution and parameter calibration using CMIP5 AOGCM experiments / O. Geoffroy, D. Saint-Martin, D.J.L. Olivie, A. Voldoire, G. Bellon, S. ytéca // Journal of Climate. – 2013. – Vol. 26, Issue 6. – P. 1841-1857. – DOI: 10.1175/JCLI-D-12-00195.1.
  2. Chen, M.Z.Q. Stabilizing solution and parameter dependence of modified algebraic riccati equation with application to discrete-time network synchronization / M.Z.Q. Chen, L. Zhang, H. Su, G. Chen // IEEE Transactions on Automatic Control. – 2016. – Vol. 61, Issue 1. – P. 228-233. – DOI: 10.1109/TAC.2015.2434011.
  3. Yadav, S. Viscosity behavior of high-concentration monoclonal antibody solutions: Correlation with interaction parameter and electroviscous effects / S. Yadav, S.J. Shire, D.S. Kalonia // Journal of Pharmaceutical Sciences. – 2012. – Vol. 101, Issue 3. – P. 998-1011. – DOI: 10.1002/jps.22831.
  4. Zhou, F. Distortion correction using a single image based on projective invariability and separate model / F. Zhou, Y. Cui, L. Liu, H. Gao // Optik – International Journal for Light and Electron Optics. – 2013. – Vol. 124, Issue 17. – P. 3125-3130. – DOI: 10.1002/jps.22831.
  5. Hou, J. Distortion correction for imaging on non-planar surface using freeform lens / J. Hou, H. Li, Z. Zheng, X. Liu // Optics Communications. – 2012. – Vol. 285, Issue 6. – P. 986-991. – DOI: 10.1016/j.optcom.2011.12.014.
  6. Dammann, P. Evaluation of hardware-related geometrical distortion in structural MRI at 7 Tesla for image-guided applications in neurosurgery / P. Dammann, O. Kraff, K.H. Wrede, N. Özkan, S. Orzada, O.M. Mueller, I.E. Sandalcioglu, U. Sure, E.R. Gizewski, M.E. Ladd, T. Gasser // Academic Radiology. – 2011. – Vol. 18, Issue 7. – P. 910-916. – DOI: 10.1016/j.acra.2011.02.011.
  7. Kaynig, V. Fully automatic stitching and distortion correction of transmission electron microscope images / V. Kaynig, B. Fischer, E. Müller, J.M. Buhmann // Journal of Structural Biology. – 2010. – Vol. 171, Issue 2. – P. 163-173. – DOI: 10.1016/j.jsb.2010.04.012.
  8. Melo, R. A new solution for camera calibration and real-time image distortion correction in medical endoscopy-initial technical evaluation / R. Melo, J.P. Barreto, G. Falcao // IEEE Transactions on Biomedical Engineering. – 2012. – Vol. 59, Issue 3. – P. 634-644. – DOI: 10.1109/TBME.2011.2177268.
  9. Siedlecki, D. Distortion correction of OCT images of the crystalline lens: Gradient index approach / D. Siedlecki, A. de Castro, E. Gambra, S. Ortiz, D. Borja, S. Uhlhorn, F. Manns, S. Marcos, J.M. Parel // Optometry and Vision Science. – 2012. – Vol. 89, Issue 5. – P. 709-718. – DOI: 10.1097/OPX.0b013e3182508344.
  10. Ding, H. Image-based spectral distortion correction for photon-counting x-ray detectors / H. Ding, S. Molloi // Medical Physics. – 2012. – Vol. 39, Issue 4. – P. 1864-1876. – DOI: 10.1118/1.3693056.
  11. Zhou, F. Line-based camera calibration with lens distortion correction from a single image / F. Zhou, Y. Cui, H. Gao, Y. Wang // Optics and Lasers in Engineering. – 2013. – Vol. 51, Issue 12. – P. 1332-1343. – DOI: 10.1016/j.optlaseng.2013.05.010.
  12. Hou, J. Distortion correction for imaging on non-planar surface using freeform lens / J. Hou, H. Li, Z. Zheng, X. Liu // Optics Communications. – 2012. – Vol. 285, Issue 6. – P. 986-991. – DOI: 10.1016/j.optcom.2011.12.014.
  13. Yu, G. An algorithm for estimation and correction of anisotropic magnification distortion of cryo-em images without need of pre-calibration / G. Yu, K. Li, Y. Liu, Z. Chen, Z. Wang, R. Yan, T. Klose, L. Tang, W. Jiang // Journal of Structural Biology. – 2016 – Vol. 195, Issue 2. – P. 207-215. – DOI: 10.1016/j.jsb.2016.06.003.
  14. Tan, T.L. Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection / T.L. Tan, K.S. Sim, C.P. Tso, A.K. Chong // International Journal of Imaging Systems and Technology. – 2012. – Vol. 22, Issue 3. – P. 153-160. – DOI: 10.1002/ima.22016.
  15. Du, Y. Suppressing gray-scale elements in topology optimization of continua using modified optimality criterion methods / Y. Du, D. Chen // Computer Modeling in Engineering and Sciences. – 2012. – Vol. 86, Issue 1. – P. 53-70. – DOI: 10.3970/cmes.2012.086.053.

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