Advertisement

On Image Enhancement for Unsupervised Image Description and Matching

  • Michela Lecca
  • Alessandro TorresaniEmail author
  • Fabio Remondino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11752)

Abstract

An image enhancer improves the visibility and readability of the content of any input image by modifying one or more features related to vision perception. Its performance is usually assessed by quantifying and comparing the level of these features in the input and output images and/or with respect to a gold standard, often regardless of the application in which the enhancer is invoked. Here we provide an empirical evaluation of six image enhancers in the specific context of unsupervised image description and matching. To this purpose, we use each enhancer as pre-processing step of the well known algorithms SIFT and ORB, and we analyze on a public image dataset how the enhancement influence image retrieval. Our analysis shows that improving perceptual features like image brightness, contrast and regularity increases the accuracy of SIFT and ORB. More generally, our study provides a scheme to evaluate image enhancement from an application viewpoint, promoting an aware usage of the evaluated enhancers in a specific computer vision framework.

Notes

Acknowledgements

The authors would like to thank Alessio Xompero (with Fondazione Bruno Kessler, IT) and (Queen Mary University, UK), for the fruitful discussions about this topic.

References

  1. 1.
  2. 2.
    OpenCV library. https://opencv.org/
  3. 3.
    MEXICO: Multi-exposure image collection (2019). https://tev.fbk.eu/technologies/image-enhancement-datasets-and-software
  4. 4.
    Banić, N., Lončarić, S.: Light random sprays Retinex: exploiting the noisy illumination estimation. IEEE Signal Process. Lett. 20(12), 1240–1243 (2013)CrossRefGoogle Scholar
  5. 5.
    Bradski, G., Konolige, K., Rabaud, V., Rublee, E.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV 2011) (ICCV), pp. 2564–2571, November 2011Google Scholar
  6. 6.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  8. 8.
    Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lihuo, H., Fei, G., Weilong, H., Lei, H.: Objective image quality assessment: a survey. Int. J. Comput. Math. 91(11), 2374–2388 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  11. 11.
    Kamble, V., Bhurchandi, K.M.: No-reference image quality assessment algorithms: a survey. Optik 126(11), 1090–1097 (2015)CrossRefGoogle Scholar
  12. 12.
    Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Am. 1, 1–11 (1971)CrossRefGoogle Scholar
  13. 13.
    Lecca, M.: STAR: a segmentation-based approximation of point-based sampling Milano Retinex for color image enhancement. IEEE Trans. Image Process. 27(12), 5802–5812 (2018)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar
  16. 16.
    Rizzi, A., Algeri, T., Medeghini, G., Marini, D.: A proposal for contrast measure in digital images. In: 2nd European Conference on Color in Graphics, Imaging, and Vision and Sixth International Symposium on Multispectral Color Science, CGIV 2004, Aachen, pp. 187–192 (2004)Google Scholar
  17. 17.
    Rizzi, A., Bonanomi, C.: Milano Retinex family. J. Electron. Imaging 26(3), 031207 (2017)CrossRefGoogle Scholar
  18. 18.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744023_34CrossRefGoogle Scholar
  19. 19.
    Phadikar, B.S., Maity, G.K., Phadikar, A.: Full reference image quality assessment: a survey. In: Bhattacharyya, S., Sen, S., Dutta, M., Biswas, P., Chattopadhyay, H. (eds.) Industry Interactive Innovations in Science, Engineering and Technology. LNNS, vol. 11, pp. 197–208. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-10-3953-9_19CrossRefGoogle Scholar
  20. 20.
    Wang, S., Zheng, J., Hu, H.-M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fondazione Bruno Kessler - ICTTrentoItaly
  2. 2.Department of Computer ScienceUniversità degli Studi di TrentoTrentoItaly

Personalised recommendations