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


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.

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  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)

  3. 3.

    Kedzierski, M.; Wierzbicki, D.: Methodology of improvement of radiometric quality of images acquired from low altitudes. Measurement 92, 70–78 (2016).

    Article  Google 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)

    Article  Google 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)

  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)

  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)

  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).

    Article  Google 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).

    Article  Google 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).

    Article  Google 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).

    Article  Google 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).

    Article  MATH  Google 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)

  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)

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Correspondence to Ali Mahdinezhad Gargari.

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Mahdinezhad Gargari, A., Ebadi, H. & Esmaeili, F. An Efficient Algorithm for Detection of Image Stretching Error from a Collection of Images Acquired by Unmanned Aerial Vehicles. Arab J Sci Eng 44, 489–504 (2019).

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  • Blurring
  • Detection
  • Image stretching
  • Photogrammetric
  • Radiometric errors
  • UAV (unmanned aerial