Volume 32 #1

Contents

1. Variability of different functional songs types from Aelia acuminata L. (Heteroptera, Pentatomidae)
2. Receptive field sizes of sustained ganglion cells in the retina of Carassius gibelio
3. Fusion of radar, visible and thermal imagery with account for differences in brightness and chromaticity perception
4. Modelling of the unmanned aerial vehicle navigation on the basis of two height-shifted onboard cameras
5. Visual localisation of aerial images on vector map using colour-texture segmentation
6. Comparison of the digitized pages of business documents by means of recognition
7. The algorithm of epipole position estimation under pure camera translation
8. Segmentation of vehicle registration plates based on dynamic time warping
9. Finding the correspondence between closed curves under projective distortions
10. Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries
11. Weighted search for a projective optical flow resistant to specular reflections
12. Algebraic methods for tomography problem
13. Quantitative analysis of the point light source virtual image doubling in underwater stereo vision

Motion blur is a common source of degradation in photographs because of camera shake. In this paper, the task of definition point spread function of blurred image via tomography technique is given. The result of the point spread function (PSF) reconstruction strongly depends on the used tomography technique. We present a new tomography algorithm that adapts for this task. We use algebraic reconstruction technique (ART algorithm) as initial algorithm and add regularization term for qualitative result. We use conjugate gradient method for numerical implementation of the proposed approach. The algorithm is tested using dataset which contains 9 kernels extracted from real photographs by Adobe corporation. We also investigate influence of noise on the quality of image reconstruction and investigate how the number of projections influence on the magnitude change of the reconstruction error. The proposed approach is tested on real photographs where the point spread function is known.

Key words: Motion blur, Point Spread Function, Algebraic Reconstruction Tecniques, regularization

DOI: 10.7868/S0235009218010109

References:

• Chereau R., Breckon T. P. Robust Motion Filtering as an Enabler to Video Stabilization for a Teleoperated Mobile Robot. Proc. SPIE Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII (PDF). 2013. P. 1–17. DOI: 10.1.1.391.4308.
• Cho T. S., Paris S., Horn B., Freeman W. T. Blur kernel estimation using the radon transform. Computer Vision and Pattern Recognition (CVPR). IEEE. 2011. P. 241–248. DOI: 10.1109/CVPR.2011.5995479.
• Gordon R., Bender R., Herman G.T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. Journal of Theoretical Biology. 1970. V. 29. № 3. P. 471–81. DOI: 10.1016/0022-5193(70)90109-8.
• Karnaukhov V., Mozerov M. Motion blur estimation based on multitarget matching model. Optical Engineering. 2016. V. 55. № 10. P. 1–4. DOI: 10.1117/1.OE.55.10.100502.
• Katsaggelos A.K., Lay K.T. Maximum likelihood blur identification and image restoration using the em algorithm. IEEE Trans. Signal Processing. 1991. V. 39. № 3. P. 729–733. DOI: 10.1109/78.80894.
• Kober V., Karnaukhov V. Restoration of Multispectral Images Distorted by Spatially Nonuniform Camera Motion. Journal of Communications Technology and Electronics. 2015. V. 60. № 12. P. 1366–1371. DOI:10.1134/S1064226915120153.
• Le Digabel S. Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm. ACM Transactions on Mathematical Software. 2011. V. 37. № 4. P. 44:1–44:15. DOI: 10.1145/1916461.1916468.
• Le Digabel S., Tribes C. The NOMAD software for blackbox optimization. GERAD Newsletter. 2012. V. 9. № 2. P. 6–7
• Libin S., Cho S., Jue W., Hays J. Edge-based Blur Kernel Estimation Using Patch Priors. Proceedings of the IEEE International Conference on Computational Photography (ICCP) 2013. 15 p. DOI: 10.1109/ICCPhot.2013.6528301.
• Miller S.W., Beard D.C., Brodie D.S., Hubert A.R. Magnetic camera component mounting in cameras. Google Patents US9591221 B2. 2017.
• Miyoshi K. Image stabilization apparatus, optical apparatus, and imaging apparatus. Google Patents US9557575 B2.2017.
• Natterer F. The Mathematics of Computerized Tomography. Philadelphia. SIAM, 1986. 221 p.
• Oliveira J.P., Figueiredo M.A., Bioucas-Dias J.M. Blind estimation of motion blur parameters for image deconvolution. Iberian Conference on Pattern Recognition and Image Analysis. Springer, 2007. P. 604–611. DOI:10.1109/TIP.2013.2286328.
• Rekleitis I.M. Steerable filters and cepstral analysis for optical flow calculation from a single blurred image. Vision Interface. 1996. V. 1. P. 159–166.
• Rothenberg M. A new inverse‐filtering technique for deriving the glottal air flow waveform during voicing. The Journal of the Acoustical Society of America. 1973. V. 53. № 6. P. 1632–1645. DOI:10.1121/1.1975066.
• van Aarle W., Palenstijn W. J., De Beenhouwer J., Altantzis T., Bals S., Batenburg K.J., Sijbers J. The astra toolbox: A platform for advanced algorithm development. Electron tomography Ultramicroscopy. 2015. 157:35–47. DOI:10.1016/j.ultramic.2015.05.002.
• van Aarle W., Palenstijn W. J., Cant J., Janssens E., Bleichrodt F., Dabravolski A., De Beenhouwer J., Batenburg K.J., Sijbers J. Fast and Flexible X-ray Tomography Using the ASTRA Toolbox. Optics Express. 2016. V. 24. № 22. P. 25129–25147. DOI:10.1364/OE.24.025129.