An improved PDE-constrained optimization fluid registration for image multi-frame super resolution
RAIRO. Operations Research, Tome 56 (2022) no. 4, pp. 3047-3069

The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on a partial differential equation (PDE)-constrained optimization fluid image registration and we use a fourth order PDE to treat both the registration and restoration steps that guarantee the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.

DOI : 10.1051/ro/2022137
Classification : 49-XX, 49MXX, 49M41
Keywords: Super resolution, bilevel PDE, fluid registration, image restoration, regularization
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     editor = {Mahjoub, A. Ridha and Laghrib, A. and Metrane, A.},
     title = {An improved {PDE-constrained} optimization fluid registration for image multi-frame super resolution},
     journal = {RAIRO. Operations Research},
     pages = {3047--3069},
     year = {2022},
     publisher = {EDP-Sciences},
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Laghrib, Amine; Hadri, Aissam; Hakim, Moad; Oummi, Hssaine. An improved PDE-constrained optimization fluid registration for image multi-frame super resolution. RAIRO. Operations Research, Tome 56 (2022) no. 4, pp. 3047-3069. doi: 10.1051/ro/2022137

[1] M. Alahyane, A. Hakim, A. Laghrib and S. Raghay, A fast approach of nonparametric elastic image registration problem. Math. Methods Appl. Sci. 42 (2019) 7059–7075. | MR | Zbl | DOI

[2] J. P. Ardila, V. A. Tolpekin, W. Bijker and A. Stein, Markov-random-field-based super-resolution mapping for identification of urban trees in vhr images. ISPRS J. Photogramm. Remote Sens. 66 (2011) 762–775. | DOI

[3] G. Aubert and P. Kornprobst, Mathematical problems in image processing: partial differential equations and the calculus of variations, Vol. 147. Springer Science & Business Media (2006). | MR | Zbl

[4] J.-P. Aubin, Un théorème de compacité. Acad. Sci. Paris 256 (1963) 5042–5044. | MR | Zbl

[5] R. M. Bahy, G. I. Salama and T. A. Mahmoud, Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images. Signal Process. 103 (2014) 155–167. | DOI

[6] S. Baker and T. Kanade, Super-resolution optical flow. Carnegie Mellon University, The Robotics Institute (1999).

[7] S. Baker and T. Kanade, Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1167–1183. | DOI

[8] M. Bergounioux and L. Piffet, A second-order model for image denoising. Set-Valued Var. Anal. 18 (2014) 277–306. | MR | Zbl | DOI

[9] H. Brezis, Functional Analysis, Sobolev Spaces and Partial Differential Equations. Springer (2011). | MR | Zbl | DOI

[10] A. Buades, B. Coll and J.-M. Morel, The staircasing effect in neighborhood filters and its solution. IEEE Trans. Image Process. 15 (2006) 1499–1505. | DOI

[11] A. Chambolle and P.-L. Lions, Image recovery via total variation minimization and related problems. Numer. Math. 76 (1997) 167–188. | MR | Zbl | DOI

[12] F. Demengel and G. Demengel, Espaces fonctionnels. Utilisation dans la résolution des équations aux dérivées partielles. CNRS Editions (2007). | MR | Zbl

[13] I. El Mourabit, M. El Rhabi, A. Hakim, A. Laghrib and E. Moreau, A new denoising model for multi-frame super-resolution image reconstruction. Signal Process. 132 (2017) 51–65. | DOI

[14] I. El Mourabit, A. Hakim and A. Laghrib, An anisotropic pde for multi-frame super-resolution image reconstruction. In International Conference on Numerical Analysis and Optimization Days. Springer (2021) 29–41. | MR

[15] S. Farsiu, M. D. Robinson, M. Elad and P. Milanfar, Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13 (2004) 1327–1344. | DOI

[16] R. Fransens, C. Strecha and L. Van Gool, Optical flow based super-resolution: A probabilistic approach. Comput. Vis. Image Underst. 106 (2007) 106–115. | DOI

[17] D. Ghosh, N. Kaabouch and W.-C. Hu, A robust iterative super-resolution mosaicking algorithm using an adaptive and directional huber-markov regularization. J. Vis. Commun. Image Represent. 40 (2016) 98–110. | DOI

[18] H. Greenspan, G. Oz, N. Kiryati and S. L. B. G. Peled, Mri inter-slice reconstruction using super-resolution. Magn. Reson. Imaging 20 (2002) 437–446. | DOI

[19] Y. Han, C. Xu, G. Baciu and X. Feng, Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer. Int. J. Comput. Math. 91 (2014) 2243–2259. | MR | Zbl | DOI

[20] Y. He, K.-H. Yap, L. Chen and L.-P. Chau, A nonlinear least square technique for simultaneous image registration and super-resolution. IEEE Trans. Image Process. 16 (2007) 2830–2841. | MR | DOI

[21] Q. Huynh-Thu and M. Ghanbari, Scope of validity of psnr in image/video quality assessment. Electron. Lett. 44 (2008) 800–801. | DOI

[22] M. Jung, A. Marquina and L. A. Vese, Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels. J. Comput. Appl. Math. 240 (2013) 123–134. | MR | Zbl | DOI

[23] N. Kumar, R. Verma and A. Sethi, Convolutional neural networks for wavelet domain super resolution. Pattern Recognit. Lett. 90 (2017) 65–71. | DOI

[24] A. Laghrib, A. Hakim and S. Raghay, A combined total variation and bilateral filter approach for image robust super resolution. EURASIP J. Image Video Process. 2015 (2015) 1–10. | DOI

[25] A. Laghrib, A. Ghazdali, A. Hakim and S. Raghay, A multi-frame super-resolution using diffusion registration and a nonlocal variational image restoration. Comput. Math. Appl. 72 (2016) 2535–2548. | MR | Zbl | DOI

[26] A. Laghrib, A. Ben-Loghfyry, A. Hadri and A. Hakim, A nonconvex fractional order variational model for multi-frame image super-resolution. Signal Process. Image Commun. 67 (2018) 1–11. | DOI

[27] A. Laghrib, M. Ezzaki, M. El Rhabi, A. Hakim, P. Monasse and S. Raghay, Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution. Comput. Vis. Image Underst. 168 (2018) 50–63. | DOI

[28] A. Laghrib, A. Hadri and A. Hakim, An edge preserving high-order pde for multiframe image super-resolution. J. Franklin Inst. 356 (2019) 5834–5857. | MR | Zbl | DOI

[29] A. Laghrib, A. Hadri, A. Hakim and S. Raghay, A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization. Inf. Sci. 493 (2019) 34–56. | MR | Zbl | DOI

[30] A. Laghrib, A. Chakib, A. Hadri and A. Hakim, A nonlinear fourth-order pde for multi-frame image super-resolution enhancement. Discrete Contin. Dyn. Syst.-B 25 (2020) 415. | Zbl | MR

[31] R. Lai, X.-C. Tai and T. F. Chan, A ridge and corner preserving model for surface restoration. SIAM J. Sci. Comput. 35 (2013) A675–A695. | MR | Zbl | DOI

[32] L. F. Lang, S. Neumayer, O. Öktem and C.-B. Schönlieb, Template-based image reconstruction from sparse tomographic data. Appl. Math. Optim. (2019) 1–29. | MR | Zbl

[33] S. Lefkimmiatis, A. Bourquard and M. Unser, Hessian-based norm regularization for image restoration with biomedical applications. IEEE Trans. Image Process. 21 (2012) 983–995. | MR | Zbl | DOI

[34] M. Lysaker and X.-C. Tai, Iterative image restoration combining total variation minimization and a second-order functional. Int. J. Comput. Vis. 66 (2006) 5–18. | Zbl | DOI

[35] B. J. Maiseli, N. Ally and H. Gao, A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method. Signal Process. Image Commun. 34 (2015) 1–13. | DOI

[36] J. Modersitzki, Numerical Methods for Image Registration. Oxford University Press, USA (2007). | MR

[37] K. Papafitsoros and C.-B. Schönlieb, A combined first and second order variational approach for image reconstruction. J. Math. Imaging Vis. 48 (2014) 308–338. | MR | Zbl | DOI

[38] M. K. Park and M. G. Kang, Regularized high-resolution reconstruction considering inaccurate motion information. Opt. Eng. 46 (2007) 117004. | DOI

[39] M. Protter, M. Elad, H. Takeda and P. Milanfar, Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans. Image Process. 18 (2008) 36–51. | MR | Zbl | DOI

[40] P. Rasti, H. Demirel and G. Anbarjafari, Improved iterative back projection for video super-resolution. In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE (2014) 552–555. | DOI

[41] M. D. Robinson, S. J. Chiu, C. A. Toth, J. A. Izatt, J. Y. Lo and S. Farsiu, New applications of super-resolution in medical imaging. In Super-Resolution Imaging. CRC Press (2017) 401–430.

[42] D. A. Sorrentino and A. Antoniou, Storage-efficient quasi-newton algorithms for image super-resolution. In 2009 16th International Conference on Digital Signal Processing. IEEE (2009) 1–6.

[43] H. Su, N. Jiang, Y. Wu and J. Zhou, Single image super-resolution based on space structure learning. Pattern Recognit. Lett. 34 (2013) 2094–2101. | DOI

[44] T. Valkonen, K. Bredies and F. Knoll, Total generalized variation in diffusion tensor imaging. SIAM J. Imaging Sci. 6 (2013) 487–525. | MR | Zbl | DOI

[45] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (2004) 600–612. | DOI

[46] F. W. Wheeler, R. T. Hoctor and E. B. Barrett, Super-resolution image synthesis using projections onto convex sets in the frequency domain. In Vol. 5674 of Computational Imaging III. International Society for Optics and Photonics (2005) 479–490.

[47] S. Yang, M. Wang, Y. Sun, F. Sun and L. Jiao, Compressive sampling based single-image super-resolution reconstruction by dual-sparsity and non-local similarity regularizer. Pattern Recognit. Lett. 33 (2012) 1049–1059. | DOI

[48] X. Yang and J. Yang, Efficient diffeomorphic metric image registration via stationary velocity. J. Comput. Sci. 30 (2019) 90–97. | DOI

[49] Q. Yuan, L. Zhang and H. Shen, Multiframe super-resolution employing a spatially weighted total variation model. IEEE Trans. Circuits Syst. Video Technol. 22 (2012) 379–392. | DOI

[50] L. Yue, H. Shen, J. Li, Q. Yuan, H. Zhang and L. Zhang, Image super-resolution: The techniques, applications, and future. Signal Process. 128 (2016) 389–408. | DOI

[51] W. Zhao, H. Sawhney, M. Hansen and S. Samarasekera, Super-fusion: a super-resolution method based on fusion. In Vol. 2 of Object recognition supported by user interaction for service robots. IEEE (2002) 269–272. | DOI

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