УДК: 004.932.4

Adaptive algorithms for superresolution based on processing a sequence of images

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Сирота А.А., Иванков А.Ю. Адаптивные алгоритмы построения сверхразрешения на основе обработки последовательности изображений // Оптический журнал. 2017. Т. 84. № 5. С. 38–45.

Sirota A.A., Ivankov A.Yu. Adaptive algorithms for superresolution based on processing a sequence of images [in Russian] // Opticheskii Zhurnal. 2017. V. 84. № 5. P. 38–45.

For citation (Journal of Optical Technology):

A. A. Sirota and A. Y. Ivankov, "Adaptive algorithms for superresolution based on processing a sequence of images," Journal of Optical Technology. 84(5), 316-322 (2017). https://doi.org/10.1364/JOT.84.000316

Abstract:

The problem of the synthesis of an adaptive nonlinear algorithm for filtering a sequence of images utilized for achieving superresolution under the conditions of unknown parameters accompanying the process of observation is considered. Using the method of partitioning, algorithms for adaptive filtering in a block form are developed and studied. In this approach, adaptation is performed with respect to unknown values of the parameters of interframe displacements (parameters of an affine transformation). The quality of reconstructed images is improved compared with the reconstruction quality using known algorithms based on the use of fixed estimates of the displacements over the images of the processed sequence with low resolution.

Keywords:

image processing, superresolution, optimal filtering, method of partitioning

OCIS codes: 100.2000

References:

1. M. Elad and A. Feuer, “Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images,” IEEE Trans. Image Process 6(12), 1646–1658 (1997).
2. L. C. Pickup, “Machine learning in multi-frame image super-resolution,” Ph.D. thesis (University of Oxford, 2007).
3. S. V. Blazhevich and V. N. Vintaev, “Mathematical model of an onboard processor for sub-pixel processing of data for remote probing of the Earth with the purpose of increasing the resolution of CKI,” Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa 9(1), 32–38 (2012).
4. A. Yu. Ivankov and A. A. Sirota, “Block algorithms for processing images based on the Kalman filter in the problem of constructing superresolution,” Komput. Opt. 38(1), 118–126 (2014).
5. É. A. Kirsanov and A. A. Sirota, Information Processing in Spatially Distributed Systems for Radio Monitoring: Statistical and Neural Network Approaches (Fizmatlit, Moscow, 2012).
6. R. L. Lagendijk and J. Biemond, Iterative Identification and Restoration of Images (Kluwer, Boston, 1991).
7. D. G. Lainiotis and S. K. Park, “On joint detection, estimation and system identification: discrete data case,” Int. J. Control 17(3), 609–633 (1973).
8. A. G. Tashlinskiı˘, Estimation of the Parameters of Spatial Deformations of Image Sequences (UlSTU, Ul’yanovsk, 2000).
9. É. K. Algazinov and A. A. Sirota, Analysis and Computer Modeling of Information Processes and Systems (Dialog-MIFI, Moscow, 2009).