Abstract
In image-related applications, the recorded images are the blurry version of the original image that usually depicts any scene. Due to the optical aberrations, atmospheric distortions, and motion of objects in the scene, blur occurs in the images and degrades image quality. Various image deblurring methods are modeled in the existing works, but accurately recovering the exact true image from the single recorded image or the set of images still results in challenging medical imaging applications. Thus, an effective image deblurring method named Exponential-Ant Cuckoo Search Optimization (Exponential-ACSO) is developed in this research to find the spatial information from the blurred image. The proposed Exponential-ACSO is designed by integrating the Exponential Weighted Moving Average (EWMA) with Ant Lion Optimization (ALO) and Cuckoo Search (CS) algorithm. The computation of new pixel values for noisy pixels makes the image deblurring process more robust and accurate. The objective function is considered to find the optimal fitness value for the parameters of kernel estimation. However, the proposed Exponential-ACSO showed better performance for the metrics, like peak signal-to-noise ratio (PSNR), second derivative like the measure of enhancement (SDME), and structural similarity index (SSIM) with the values 29.756, 33.562, and 0.6988, respectively.
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References
Huang, J., Huang, T.Z.: A nonstationary accelerating alternating direction method for frame-based Poissonian image deblurring. J. Comput. Appl. Math. 352, 181–193 (2019)
Sharma, P., Dubey, A.K., Goyal, A.: Efficient image deblurring using alpha plane blending on images recovered with linearly varied point spread function (PSF). In: Hu, Y.-C., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Ambient Communications and Computer Systems, pp. 509–522. Springer, Singapore (2019)
Ljubenović, M., Figueiredo, M.A.: Plug-and-play approach to class-adapted blind image deblurring. Int. J. Doc. Anal. Recognit. (IJDAR) 22(2), 79–97 (2019)
Ople, J.J.M., Yeh, P.Y., Sun, S.W., Tsai, I.T., Hua, K.L.: Multi-scale neural network with dilated convolutions for image deblurring. IEEE Access 8, 53942–53952 (2020)
Miao, S. and Zhu, Y., "Handling noise in image deblurring via joint learning", arXiv preprint arXiv:2001.09730, 2020.
Ma, T.H., Huang, T.Z., Zhao, X.L., Lou, Y.: Image deblurring with an inaccurate blur kernel using a group-based low-rank image prior. Inf. Sci. 408, 213–233 (2017)
Lu, B., Chen, J.C. and Chellappa, R., Unsupervised domain-specific deblurring via disentangled representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10225–10234, (2019)
Adam, T., Paramesran, R.: Hybrid non-convex second-order total variation with applications to non-blind image deblurring. SIViP 14(1), 115–123 (2020)
Li, D.W., Lai, L.J., Huang, H.: Defocus hyperspectral image deblurring with adaptive reference image and scale map. J. Comput. Sci. Technol. 34(3), 569–580 (2019)
Peña, F.A.G., Fernández, P.D.M., Ren, T.I., Leandro, J.D.J.G., Nishihara, R.M.: Burst ranking for blind multi-image deblurring. IEEE Trans. Image Process. 29, 947–958 (2019)
Zhang, X., Wang, R., Jiang, X., Wang, W., Gao, W.: Spatially variant defocus blur map estimation and deblurring from a single image. J. Vis. Commun. Image Represent. 35, 257–264 (2016)
Chen, H., Fan, Y., Wang, Q., Li, Z.: Morphological component image restoration by employing bregmanized sparse regularization and anisotropic total variation. Circuits, Syst. Signal Process. 39, 1–26 (2019)
Yang, C., Wang, W., Feng, X., Liu, X.: Weighted-l1-method-noise regularization for image deblurring. Signal Process. 157, 14–24 (2019)
Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven M. Levy, Ge Wang, James C. Torner, and Punam K. Saha, Deep learning based high-resolution reconstruction of trabecular bone microstructures from lowresolution CT scans using GAN-CIRCLE. In: Proc. SPIE Medical Imaging, vol. 11317, (2020)
Mousa, M.A., Li, H.B., Abdalla, H.B.: Optimization driven adam-cuckoo search-based deep belief network classifier for data classification. IEEE Access 8(1), 105542 (2020)
AI Sibahee, M.A., Abdalla, H.B., Ahmed, A.M.: Optimization driven MapReduce framework for indexing and retrieval of big data. KSII Trans. Internet Inf. Syst. (2020). https://doi.org/10.3837/tiis.2020.05.002
Alam, M.Z., Qian, Q., Gunturk, B.K.: Space-variant blur kernel estimation and image deblurring through kernel clustering. Signal Process.: Image Commun. 76, 41–55 (2019)
He, L., Wang, Y., Xiang, Z.: Support driven wavelet frame-based image deblurring. Inf. Sci. 479, 250–269 (2019)
Pham, C.T., Tran, T.T.T., Gamard, G.: An efficient total variation minimization method for image restoration. Informatica 31(3), 539–560 (2020)
Wang, M., Hou, S., Li, H., Li, F.: Generative image deblurring based on multi-scaled residual adversary network driven by composed prior-posterior loss. J. Vis. Commun. Image Represent. 65, 102648 (2019)
Chen, H., Yan, K., Zhang, J. and Li, Z., Simultaneous cartoon-plus-texture image deconvolution by using variational image decomposition. In: Optoelectronic Imaging and Multimedia Technology III, International Society for Optics and Photonics, vol. 9273, p. 92732Q, (2014)
Ruud, J., Sloun, G.V., Cohen, R., Eldar, Y.C.: Deep learning in ultrasound imaging. IEEE 108(1), 11–29 (2020)
Zhang, H., Dai, Y., Li, H. and Koniusz, P., Deep stacked hierarchical multi-patch network for image deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5978–5986, (2019)
Lim, Y., Narayanan, S. and Nayak, K.S., Deblurring for spiral real-time MRI using convolutional neural networks. arXiv preprint arXiv:2001.09427, (2020)
Holla, K.S., Jidesh, P., Bini, A.A.: Multiple-coil magnetic resonance image denoising and deblurring with nonlocal total bounded variation. IETE Tech. Rev. 37, 1–6 (2019)
Peng, J., Shao, Y., Sang, N., Gao, C.: Joint image deblurring and matching with feature-based sparse representation prior. Pattern Recognit. 103, 107300 (2020)
Saccucci, M.S., Amin, R.W., Lucas, J.M.: Exponentially weighted moving average control schemes with variable sampling intervals. Commun. Statistics-Simulation Comput. 21(3), 627–657 (1992)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Computers 29(1), 17–35 (2013)
Chena, Z., Chena, Y., Wua, L., Chenga, S., Lin, P.: Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Convers. Manage. 198, 111793 (2019)
Pan, J., Liu, R., Su, Z., Gu, X.: Kernel estimation from salient structure for robust motion deblurring. Signal Process.: Image Commun. 28(9), 1156–1170 (2013)
Osteoporotic vertebral fractures dataset, "http://spineweb.digitalimaginggroup.ca/spineweb/index.php?n=Main.Datasets#Dataset_9.3A_Automatic_vertebral_fracture_analysis_and_identification_from_VFA_by_DXA", accessed on May 2020.
Pan, J., Sun, D., Pfister, H., Yang, M.-H.: De-blurring images via dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 40(10), 2315–2328 (2017)
Lim Y, Narayanan S, Nayak KS, De-blurring for spiral real-time MRI using convolutional neural networks. arXiv preprint, (2020)
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Priya, S., Letitia, S. Exponential-Ant Cuckoo Search Optimization for image deblurring with spinal cord images based on kernel estimation. SIViP 16, 339–347 (2022). https://doi.org/10.1007/s11760-021-01929-y
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DOI: https://doi.org/10.1007/s11760-021-01929-y