Abstract
In the process of image denoising based on anisotropic diffusion model, the problem of edge information loss and “staircase effect” often appear. On the basis of anisotropic diffusion model, this paper combines the fractional diffusion model with the gradient based integer diffusion model, and introduces washout filter as the control term of the model, a new image super-resolution reconstruction algorithm based on hybrid diffusion model is proposed. In our proposed model, the fractional derivative will adjust its size adaptively according to the local variance of the image, and because the threshold k in the traditional diffusion function requires a lot of data experiments to get the best results, we also propose an adaptive threshold k function, whose value changes adaptively with the gradient of the image. Simulation results show that, compared with other algorithms, the new model still has a strong ability to retain image details and edge information after image reconstruction, and the introduced washout filter will also speed up the rapid convergence of the system to a stable state, and improve the convergence speed and stability of the system.
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References
Yang, X., Xie, T., Liu, L.: Image super-resolution reconstruction based on improved Dirac residual network. Multidimens. Syst. Signal Process. 1–18 (2021)
Zhao, X., Zhang, X.: Multi-frame super-resolution reconstruction algorithm of optical remote sensing images based on double regularization terms and unsupervised learning. Int. J. Pattern Recogn. Artif. Intell. 35, 2154002 (2020)
Laghrib, A., Hadri, A., Hakim, A.: An edge preserving high-order PDE for multiframe image super-resolution. J. Franklin Inst. 356, 5834–5857 (2019)
Khan, A., Khan, M.A.: A novel multi-frame super resolution algorithm for surveillance camera image reconstruction. In: First International Conference on Anti-cybercrime. IEEE (2015)
Afraites, L., Hadri, A., Laghrib, A.: A high order PDE-constrained optimization for the image denoising problem. Inverse Probl. Sci. Eng. 1–43 (2020)
Tian, C., Chen, Y.: Image segmentation and denoising algorithm based on partial differential equations. IEEE Sens. J. 20, 11935–11942 (2020)
Golbaghi, F.K., Rezghi, M., Eslahchi, M.R.: A hybrid image denoising method based on integer and fractional-order total variation. Iran. J. Sci. Technol. Trans. A Sci. 44(3), 1803–1814 (2020)
Chen, Y., He, T.: Image denoising via an adaptive weighted anisotropic diffusion. Multidimension. Syst. Signal Process. 32(2), 651–669 (2021). https://doi.org/10.1007/s11045-020-00760-x
Bai, J., Feng, X.C.: Fractional-order anisotropic diffusion for image denoising. IEEE Trans. Image Process. 16, 2492–2502 (2007)
Yin, X.: Fractional-order difference curvature-driven fractional anisotropic diffusion equation for image super-resolution. Int. J. Model. Simul. Sci. Comput. 10(01), 1941012 (2019)
Malik, J.M., Perona, P.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Zhang, Y., Sun, J.: An improved BM3D algorithm based on anisotropic diffusion equation. Math. Biosci. Eng. 17(5), 4970–4989 (2020)
Surya Prasath, V.B., Delhibabu, R.: Image restoration with fuzzy coefficient driven anisotropic diffusion. In: Panigrahi, B.K., Suganthan, P.N., Das, S. (eds.) SEMCCO 2014. LNCS, vol. 8947, pp. 145–155. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20294-5_13
Dong, W., Zhang, L., Shi, G., Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011)
Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8
Dong, C., Loy, C.C., He, K.: Image super-resolution using deep convolutional network. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)
Bai, J., Feng, X.-C.: Image denoising using generalized anisotropic diffusion. J. Math. Imaging Vis. 60(7), 994–1007 (2018). https://doi.org/10.1007/s10851-018-0790-4
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Yu, J., Yin, J., Huang, S., Qin, M., Yang, X., Zhou, S. (2021). A New Image Super-Resolution Reconstruction Algorithm Based on Hybrid Diffusion Model. In: Wang, Y., Song, W. (eds) Image and Graphics Technologies and Applications. IGTA 2021. Communications in Computer and Information Science, vol 1480. Springer, Singapore. https://doi.org/10.1007/978-981-16-7189-0_14
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DOI: https://doi.org/10.1007/978-981-16-7189-0_14
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