Image registration comparative analysis: normalized correlation versus SIFT-based registration


  • V.A. Dushepa Національний аерокосмічний університет ім. М. Є. Жуковського «Харківський авіаційний інститут», Ukraine
  • Y.A. Tiahnyriadno Національний аерокосмічний університет ім. М. Є. Жуковського «Харківський авіаційний інститут», Ukraine
  • I.V. Baryshev Національний аерокосмічний університет ім. М. Є. Жуковського «Харківський авіаційний інститут», Ukraine



image registration algorithms, normalized correlation, SIFT, python, OpenCV


The paper compares the image registration algorithms: the classical normalized correlation (as a representative of intensity-based algorithms) and the SIFT-based algorithm (feature-based registration). A gradient subpixel correction algorithm was also used for normalized correlation. We compared the effectiveness of their work on real images (including a terrain map) when modeling artificial distortions. The accuracy of determining the position (shift) of one image relative to another in the presence of rotation and scale changes was studied. The experiment was carried out using a simulation model created in the Python programming language using the OpenCV computer vision library.

The results of the experiments show that in the absence of rotation and scale changes between the registered images the normalized correlation provides a slightly smaller root-mean-square error. At the same time, if there are even small such distortions, for example, a rotation of more than 2 degrees and a scale change of more than 2 percent, the probability of correct registration for the normalized correlation drops sharply. It was also noted that the advantages of normalized correlation are almost 5 times higher speed and the possibility of using it for small fragments (50x50 or less), where it is problematic for the SIFT algorithm to allocate a sufficient number of keypoints.

It was also shown that the use of a two-stage algorithm (SIFT-based registration at the first stage, and optimization with normalized correlation as a criterion at the second) allows you to get both high accuracy and stability to rotation and scale change, but this will be accompanied by high computational costs.


Zitová B., Flusser J. Image registration methods: A survey // Image Vis. Comput. 2003. Vol. 21 (11). P. 977 – 1000.

Brown L. G. A survey of image registration techniques // ACM Comput. Surv. 1992. Vol. 24, No. 4. P. 325–376.

Uss M. L., Vozel B., Lukin V. V., Chehdi K. Efficient discrimination and localization of multimodal remote sensing images using CNN-based prediction of localization uncertainty // Remote Sensing. 2020. Vol. 12, No. 4., 703.

Conte G., Doherty P. Vision-based unmanned aerial vehicle navigation using georeferenced information // EURASIP J. Adv. Signal Process. 2009. Vol. 2009, Article 387308.

Душепа В., Усс М. Сравнительный анализ субпиксельных алгоритмов при совмещении изображений // Радіоелектронні і комп’ютерні системи. 2011. № 4. C. 41–51.

Gonzalez R. C., Woods R. E. Digital Image Processing, 4th ed. Pearson/Prentice Hall, 2018.

Lowe D. G. Distinctive image features from scale-invariant keypoints // International Journal of Computer Vision. 2004. Vol. 60, No. 2. P. 91–110.

Arandjelović R., Zisserman A. Three things everyone should know to improve object retrieval // 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI. 2012. P. 2911-2918.

Bradski G. The OpenCV Library // Dr. Dobb’s J. Softw. Tools. 2000.

Антюфеев В. И., Быков В. Н. Сравнительный анализ алгоритмов совмещения изображений в корреляционно-экстремальных системах навигации летательных аппаратов // Авиационно-космическая техника и технология. 2008. № 1 (48). С. 70 – 74.

Karami E., Prasad S., Shehata M. Image matching using SIFT, SURF, BRIEF, and ORB: performance comparison for distorted images // Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference, St. John’s, Canada. 2015.

Virtanen P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python // Nature Methods. 2020. Vol. 17(3). P. 261-272.

Harris C. R. et al. Array programming with NumPy // Nature. Vol. 585. P. 357–362.

SASGIS. Веб-картография и навигация [Электронный ресурс]. Режим доступа:

Dushepa V. A machine learning approach for image registration accuracy estimation // 2020 IEEE Ukrainian Microwave Week (UkrMW), Kharkiv, Ukraine. 2020. P. 368–372.




How to Cite

Dushepa, V., Tiahnyriadno, Y., & Baryshev, I. (2020). Image registration comparative analysis: normalized correlation versus SIFT-based registration. Radiotekhnika, 4(203), 191–196.