Abstract
In this paper, a high-speed hardware-based image registration is proposed exploiting parallelism and adaptive sampling technique to fulfill the requirement of high-speed portable multimedia devices. This technique computes radial and angular projections in parallel way without converting the image into polar domain, but by adjusting the number of samples along angular direction according to radius length which decreases computational load. Further, a complete image registration algorithm without using any geometric transformation is proposed. The software-based implementation of the proposed algorithm is 1.33 times faster than its latest available method in the literature. The proposed algorithm is mapped in field-programmable gate array (FPGA, Virtex6-xc6vlx760-2-ff1760) and it utilizes \(2.03\%\) Slice LUTs, \(35.14\%\) LUT-FF pair and \(1.27\%\) DSP48E1s; and maximum clock frequency is 266 Mz. The hardware-based implementation of the proposed algorithm is \(10^4\) times faster than software counterpart.
Similar content being viewed by others
References
Birk, M., Kretzek, E., Figuli, P., Weber, M., Becker, J., Ruiter, N.: High-speed medical imaging in 3D ultrasound computer tomography. IEEE Trans. Parallel Distrib. Syst. 27(2), 455–467 (2016)
Bowen, F., Hu, J., Du, E.Y.: A multistage approach for image registration. IEEE Trans. Cybern. 46(9), 2119–2131 (2016)
Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. (CSUR) 24(4), 325–376 (1992)
Castro-Pareja, C.R., Shekhar, R.: Hardware acceleration of mutual information-based 3D image registration. J. Imaging Sci. Technol. 9(2), 105–113 (2005)
Chanwimaluang, T., Fan, G.: Retinal Image Registration for NIH’s ETDRS, pp. 51–59. Springer, Berlin (2005)
Penney, G.P., Batchelor, P.G., Hill, D.L., Hawkes, D.J., Weese, J.: Validation of a two-to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images. Med. Phys. 6, 1024–1032 (2001)
Guo, F., Zhao, X., Zou, B., Ouyang, P.: 3D Reconstruction and registration for retinal image pairs. In: 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). pp. 364–368 (2018)
Han, L., Hipwell, J.H., Eiben, B., Barratt, D., Modat, M., Ourselin, S., Hawkes, D.J.: A nonlinear biomechanical model based registration method for aligning prone and supine MR breast images. IEEE Trans. Med. Imaging 33(3), 682–694 (2014)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey vision conference. vol 15. Manchester, UK, pp. 50 (1988)
Kim. J.M., Song, M.K., Kim, K.H., Lee, W.K.: Key point detection and high speed image registration using BLoG. In: 2010 Second International Conference on Intelligent Human–Machine Systems and Cybernetics, vol. 2, pp. 245–249 (2010)
Lewis, J.: Fast normalized cross-correlation. Vis. Interface 10, 120–123 (1995)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Maintz, J., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)
Matungka, R., Zheng, Y.F., Ewing, R.L.: Image registration using adaptive polar transform. Image Process. IEEE Trans. 18(10), 2340–2354 (2009)
Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996)
Sheng, Y., Lejeune, C., Arsenault, H.H.: Frequency-domain Fourier-Mellin descriptors for invariant pattern recognition. Opt. Eng. 27(5), 275354–275354 (1988)
Stone, H.S., Tao, B., McGuire, M.: Analysis of image registration noise due to rotationally dependent aliasing. J. Vis. Commun. Image Represent. 14(2), 114–135 (2003)
Traver, V.J., Pla, F.: Similarity motion estimation and active tracking through spatial-domain projections on log-polar images. Comput. Vis. Image Underst. 97(2), 209–241 (2005)
Weese, J., Goecke, R., Penney, G.P., Desmedt, P., Buzug, T.M., Schumann, H.: Fast voxel-based 2D/3D registration algorithm using a volume rendering method based on the shear-warp factorization. Proc. SPIE 3661, 802–810 (1999)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)
Zokai, S., Wolberg, G.: Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations. Image Process. IEEE Trans. 14(10), 1422–1434 (2005)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mondal, P., Banerjee, S. FPGA-accelerated adaptive projection-based image registration. J Real-Time Image Proc 18, 113–125 (2021). https://doi.org/10.1007/s11554-020-00952-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11554-020-00952-5