Advertisement

Arabian Journal for Science and Engineering

, Volume 44, Issue 1, pp 489–504 | Cite as

An Efficient Algorithm for Detection of Image Stretching Error from a Collection of Images Acquired by Unmanned Aerial Vehicles

  • Ali Mahdinezhad GargariEmail author
  • Hamid Ebadi
  • Farid Esmaeili
Research Article - Earth Sciences
  • 21 Downloads

Abstract

Over the recent years, the advantages of using unmanned aerial vehicles (UAVs) have provided fascinating working areas, particularly for photogrammetric goals. One of the main problems preventing the UAV data to achieve fully automated processing is the image stretching error and reduced resolution or image blurring, which is caused by camera shake during shooting or slow shutter speed. Movements of the sensors may be due to normal motions during the flight, strong winds, lack of proper functioning of the gimbal stabilizer or an operator’s lack of skill for properly controlling the drone. Image blurring negatively affects data interpretation and visual analysis, which in turn raises challenges for detection and matching algorithms; as a result, the precision of automatic processing and accuracy of the extracted geometrical information would decrease. Time-consuming and costly manual methods are typically adopted to identify and remove images with radiometric errors. Such methods are tedious, especially for large datasets and bring about high margin of errors. In this paper, an automatic and reliable algorithm is presented to identify and distinguish blurred images. The aim is to extract the saturation blue difference (SBD) parameter from the sets of images. It is known that SBD value has a direct relationship with the amount of blurring. The numerical value of the parameter is determined based on the variation in the extracted edge pixels of blurred and non-blurred images. Evaluation on two datasets indicates that the proposed algorithm based on the selected threshold limit (defined with regard to the geometric and visual requirements of images) for the SBD values can recognize the images with the SBD values less than the threshold value as blurred images with 100% certainty and extract them from the sets of obtained images.

Keywords

Blurring Detection Image stretching Photogrammetric Radiometric errors UAV (unmanned aerial 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, X.: Using Refined Least Square Image Matching to Improve 3D Reconstruction under Gaussian Blur and Motion Blur images. The Ohio State University, Columbus (2017)Google Scholar
  2. 2.
    Liang, X.; Wang, X.; Guo, J.; Zheng, J.: Automatic segmentation of blurry region using Haar-wavelet transform. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 15–17 Dec 2017, pp. 132–136 (2017)Google Scholar
  3. 3.
    Kedzierski, M.; Wierzbicki, D.: Methodology of improvement of radiometric quality of images acquired from low altitudes. Measurement 92, 70–78 (2016).  https://doi.org/10.1016/j.measurement.2016.06.003 CrossRefGoogle Scholar
  4. 4.
    Koik, B.T.; Ibrahim, H.: Exploration of current trend on blur detection method utilized in digital image processing. J. Ind. Intell. Inf. 1(3), 143–147 (2013)Google Scholar
  5. 5.
    Teo, T.-A.; Zhan, K.-Z.: Integration of image-derived and pos-derived features for image blur detection. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 41, 1051 (2016)CrossRefGoogle Scholar
  6. 6.
    Ong, E.; Lin, W.; Lu, Z.; Yang, X.; Yao, S.; Pan, F.; Jiang, L.; Moschetti, F.: A no-reference quality metric for measuring image blur. In: Seventh International Symposium on Signal Processing and Its Applications. Proceedings, 1–4 July 2003, vol. 461, pp. 469–472 (2003)Google Scholar
  7. 7.
    Joshi, N.; Szeliski, R.; Kriegman, D.J.: PSF estimation using sharp edge prediction. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, 23–28 June 2008, pp. 1–8 (2008)Google Scholar
  8. 8.
    Narvekar, N.D.; Karam, L.J.: A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection. In: 2009 International Workshop on Quality of Multimedia Experience, 29–31 July 2009, pp. 87–91 (2009)Google Scholar
  9. 9.
    Rahtu, E.; Heikkil, J.; Ojansivu, V.; Ahonen, T.: Local phase quantization for blur-insensitive image analysis. Image Vis. Comput. 30(8), 501–512 (2012).  https://doi.org/10.1016/j.imavis.2012.04.001 CrossRefGoogle Scholar
  10. 10.
    Sieberth, T.; Wackrow, R.; Chandler, J.H.: Automatic detection of blurred images in UAV image sets. ISPRS J. Photogramm. Remote Sens. 122, 1–16 (2016).  https://doi.org/10.1016/j.isprsjprs.2016.09.010 CrossRefGoogle Scholar
  11. 11.
    Rengarajan, V.; Rajagopalan, A.N.; Aravind, R.; Seetharaman, G.: Image registration and change detection under rolling shutter motion blur. IEEE Trans. Pattern Anal. Mach. Intell. 39(10), 1959–1972 (2017).  https://doi.org/10.1109/TPAMI.2016.2630687 CrossRefGoogle Scholar
  12. 12.
    Kieu, V.C.; Cloppet, F.; Vincent, N.: Adaptive fuzzy model for blur estimation on document images. Pattern Recognit. Lett. 86, 42–48 (2017).  https://doi.org/10.1016/j.patrec.2016.12.015 CrossRefGoogle Scholar
  13. 13.
    Pertuz, S.; Puig, D.; Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recognit. 46(5), 1415–1432 (2013).  https://doi.org/10.1016/j.patcog.2012.11.011 CrossRefzbMATHGoogle Scholar
  14. 14.
    Pech-Pacheco, J.L.; Cristóbal, G.; Chamorro-Martinez, J.; Fernández-Valdivia, J.: Diatom autofocusing in brightfield microscopy: a comparative study. In: 15th International Conference on Pattern Recognition, 2000. Proceedings, pp. 314–317. IEEE (2000)Google Scholar
  15. 15.
    Crete, F.; Dolmiere, T.; Ladret, P.; Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric, pp. 64920I–64911 (2007)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Ali Mahdinezhad Gargari
    • 1
    Email author
  • Hamid Ebadi
    • 1
  • Farid Esmaeili
    • 1
  1. 1.Department of the Photogrammetry and Remote Sensing, Geomatics and Geodesy Engineering FacultyK. N. Toosi University of TechnologyTehranIran

Personalised recommendations