Abstract
This paper proposes a computationally efficient algorithm for evaluating a sum of squared differences in the image domain in the presence of arbitrary mask configurations. Among the many potential applications of this algorithm, we consider for illustration an image inpainting task. The results show that on a diverse sample of hundreds of simulated holes in the tested images, the proposed technique is more effective than the baseline normalized cross-correlation, even when the masks are properly dealt with by the baseline.
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References
Bugeau, A., Bertalmío, M., Caselles, V., Sapiro, G.: A comprehensive framework for image inpainting. IEEE Trans. Image Process. 19(10), 2634–2645 (2010)
Chan, C.h., Pang, G.K.: Fabric defect detection by fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1267–1276 (2000)
Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)
Dondi, P., Lombardi, L., Setti, A.: Dafne: a dataset of fresco fragments for digital anastlylosis. Pattern Recogn. Lett. 138, 631–637 (2020)
Elad, M., Starck, J.L., Querre, P., Donoho, D.L.: Simultaneous cartoon and texture image inpainting using morphological component analysis (mca). Appl. Comput. Harmon. Anal. 19(3), 340–358 (2005)
Fadili, M.J., Starck, J.L., Murtagh, F.: Inpainting and zooming using sparse representations. Comput. J. 52(1), 64–79 (2009)
Hasegawa, M., Kako, T., Hirobayashi, S., Misawa, T., Yoshizawa, T., Inazumi, Y.: Image inpainting on the basis of spectral structure from 2-d nonharmonic analysis. IEEE Trans. Image Process. 22(8), 3008–3017 (2013)
Kaneko, S., Satoh, Y., Igarashi, S.: Using selective correlation coefficient for robust image registration. Pattern Recogn. 36(5), 1165–1173 (2003)
Kliangsuwan, T., Heednacram, A.: Fft features and hierarchical classification algorithms for cloud images. Eng. Appl. Artif. Intell. 76, 40–54 (2018)
Konstantinidis, D., Stathaki, T., Argyriou, V.: Phase amplified correlation for improved sub-pixel motion estimation. IEEE Trans. Image Process. 28(6), 3089–3101 (2019)
Masnou, S.: Disocclusion: a variational approach using level lines. IEEE Trans. Image Process. 11(2), 68–76 (2002)
Mousavi, P., Tavakoli, A.: A new algorithm for image inpainting in fourier transform domain. Comput. Appl. Math. 38(1), 1–9 (2019)
Orchard, J.: Efficient least squares multimodal registration with a globally exhaustive alignment search. IEEE Trans. Image Process. 16(10), 2526–2534 (2007)
Padfield, D.: Masked object registration in the fourier domain. IEEE Trans. Image Process. 21(5), 2706–2718 (2011)
Pan, W., Qin, K., Chen, Y.: An adaptable-multilayer fractional fourier transform approach for image registration. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 400–414 (2008)
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., Arsenault, H.H.: Experiments on pattern recognition using invariant fourier-mellin descriptors. JOSA A 3(6), 771–776 (1986)
Sridevi, G., Srinivas Kumar, S.: Image inpainting based on fractional-order nonlinear diffusion for image reconstruction. Circuits Systems Signal Process. 38(8), 3802–3817 (2019)
Thai, D.H., Gottschlich, C.: Simultaneous inpainting and denoising by directional global three-part decomposition: connecting variational and fourier domain-based image processing. Royal Society Open Science 5(7), 171176 (2018)
Thevenaz, P., Ruttimann, U.E., Unser, M.: A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7(1), 27–41 (1998)
Tsai, D.M., Wu, S.C., Li, W.C.: Defect detection of solar cells in electroluminescence images using fourier image reconstruction. Sol. Energy Mater. Sol. Cells 99, 250–262 (2012)
Tschumperlé, D., Deriche, R.: Vector-valued image regularization with pdes: A common framework for different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 506–517 (2005)
Wang, H., Jiang, L., Liang, R., Li, X.X.: Exemplar-based image inpainting using structure consistent patch matching. Neurocomputing 269, 90–96 (2017)
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Le Hégarat-Mascle, S., Aldea, E. (2022). Spectral Analysis of Masked Signals in the Context of Image Inpainting. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_10
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