Robust sparse representation based multi-focus image fusion with dictionary construction and local spatial consistency

Recently, sparse representation-based (SR) methods have been presented for the fusion of multi-focus images. However, most of them independently consider the local information from each image patch during sparse coding and fusion, giving rise to the spatial artifacts on the fused image. In order to overcome this issue, we present a novel multi-focus image fusion method by jointly considering information from each local image patch as well as its spatial contextual information during the sparse coding and fusion in this paper. Specifically, we employ a robust sparse representation (LR_RSR, for short) model with a Laplacian regularization term on the sparse error matrix in the sparse coding phase, ensuring the local consistency among the spatially-adjacent image patches. In the subsequent fusion process, we define a focus measure to determine the focused and de-focused regions in the multi-focus images by collaboratively employing information from each local image patch as well as those from its 8-connected spatial neighbors. As a result of that, the proposed method is likely to introduce fewer spatial artifacts to the fused image. Moreover, an over-complete dictionary with small atoms that maintains good representation capability, rather than using the input data themselves, is constructed for the LR_RSR model during sparse coding. By doing that, the computational complexity of the proposed fusion method is greatly reduced, while the fusion performance is not degraded and can be even slightly improved. Experimental results demonstrate the validity of the proposed method, and more importantly, it turns out that our LR-RSR algorithm is more computationally efficient than most of the traditional SR-based fusion methods

Multi Focus Image Fusion with variable size windows

[EN] In this paper we present the Linear Image Combination Algorithm with Variable Windows (CLI-VV) for the fusion of multifocus images. Unlike the CLI-S algorithm presented in a previous work, the CLI-VV algorithm allows to automatically determine the optimal size of the window in each pixel for the segmentation of the regions with the highest sharpness. We also present the generalized CLI-VV Algorithm for the fusion of sets of multi-focus images with more than two images. This new algorithm is called Variable Windows Multi-focus Fusion (FM-VV). The CLI-VV Algorithm was tested with 21 pairs of synthetic images and 29 pairs of real multi-focus images, and the FM-VV Algorithm on 5 trios of multi-focus images. In all the tests a competitive accuracy was obtained, with execution times lower than those reported in the literature.[ES] En este artículo presentamos el Algoritmo Combinación Lineal de Imágenes con Ventanas Variables (CLI-VV) para la fusión de imágenes multi-foco. A diferencia del Algoritmo CLI-S presentado en un trabajo anterior, el algoritmo CLI-VV permite determinar automáticamente el tamaño óptimo de la ventana en cada píxel para la segmentación de las regiones con la mayor nitidez. También presentamos la generalizado el Algoritmo CLI-VV para la fusión de conjuntos de imágenes multi-foco con más de dos imágenes. A este nuevo algoritmo lo denominamos Fusión Multi-foco con Ventanas Variables (FM-VV). El Algoritmo CLI-VV se probó con 21 pares de imágenes sintéticas y 29 pares de imágenes multi-foco reales, y el Algoritmo FM-VV sobre 5 tríos de imágenes multi-foco. En todos los ejemplos se obtuvo un porcentaje de acierto competitivos, producidos en tiempos de ejecución menores a los reportados en la literatura.Calderon, F.; Garnica-Carrillo, A.; Flores, JJ. (2018). Fusión de Imágenes Multi-Foco con Ventanas Variables. Revista Iberoamericana de Automática e Informática industrial. 15(3):262-276. https://doi.org/10.4995/riai.2017.8852OJS262276153Aslantas, V., Kurban, R., 2010. Fusion of multi-focus images using differential evolution algorithm. Expert Systems with Applications 37 (12), 8861 - 8870. https://doi.org/10.1016/j.eswa.2010.06.011Aslantas, V., Toprak, A. N., 2014. A pixel based multi-focus image fusion method. Optics Communications 332, 350 - 358. https://doi.org/10.1016/j.optcom.2014.07.044Aslantas, V., Toprak, A. N., 2017. Multi-focus image fusion based on optimal defocus estimation. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.02.003Assirati, L., Silva, N. R., Berton, L., Lopes, A. A., Bruno, O. M., 2014. Performing edge detection by difference of gaussians using q-gaussian kernels. Journal of Physics: Conference Series 490 (1), 012020. https://doi.org/10.1088/1742-6596/490/1/012020Bai, X., Zhang, Y., Zhou, F., Xue, B., 2015. Quadtree-based multi-focus image fusion using a weighted focus-measure. Information Fusion 22, 105 - 118. https://doi.org/10.1016/j.inffus.2014.05.003Calderon, F., Garnica, A., 2014. Multi focus image fusion based on linear combination of images. IEEE, pp. 1-7. https://doi.org/10.1109/ROPEC.2014.7036340Calderon, F., Garnica-Carrillo, A., Flores, J. J., 2016. Fusión de imágenes multi foco basado en la combinación lineal de imágenes utilizando imágenes incrementales. Revista Iberoamericana de Automática e Informática Industrial RIAI 13 (4), 450 - 461. https://doi.org/10.1016/j.riai.2016.07.002Cao, L., Jin, L., Tao, H., Li, G., Zhuang, Z., Zhang, Y., Feb 2015. Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. Signal Processing Letters, IEEE 22 (2), 220-224. https://doi.org/10.1109/LSP.2014.2354534Chai, Y., Li, H., Li, Z., 2011. Multifocus image fusion scheme using focused region detection and multiresolution. Optics Communications 284 (19), 4376 - 4389. https://doi.org/10.1016/j.optcom.2011.05.046De, I., Chanda, B., 2013. Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure. Information Fusion 14 (2), 136 - 146. https://doi.org/10.1016/j.inffus.2012.01.007Duan, J., Meng, G., Xiang, S., Pan, C., 2014. Multifocus image fusion via focus segmentation and region reconstruction. Neurocomputing 140, 193 - 209. https://doi.org/10.1016/j.neucom.2014.03.023Eskicioglu, A., Fisher, P., Dec 1995. Image quality measures and their performance. Communications, IEEE Transactions on 43 (12), 2959-2965. https://doi.org/10.1109/26.477498Kong, W., Lei, Y., 2017. Multi-focus image fusion using biochemical ion exchange model. Applied Soft Computing 51, 314 - 327. https://doi.org/10.1016/j.asoc.2016.11.033Kuthirummal, S., Nagahara, H., Zhou, C., Nayar, S., Jan 2011. Flexible depth of field photography. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33 (1), 58-71. https://doi.org/10.1109/TPAMI.2010.66Lewis, J. J., O'Callaghan, R. J., Nikolov, S. G., Bull, D. R., Canagarajah, N., 2007. Pixel- and region-based image fusion with complex wavelets. Information Fusion 8 (2), 119 - 130, special Issue on Image Fusion: Advances in the State of the Art. https://doi.org/10.1016/j.inffus.2005.09.006Li, H., Chai, Y., Li, Z., 2013a. Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection. Optik - International Journal for Light and Electron Optics 124 (1), 40 - 51. https://doi.org/10.1016/j.ijleo.2011.11.088Li, H., Chai, Y., Li, Z., 2013b. A new fusion scheme for multifocus images based on focused pixels detection. Machine vision and applications 24 (6), 1167-1181. https://doi.org/10.1007/s00138-013-0502-4Li, H., Manjunath, B., Mitra, S., 1995. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing 57 (3), 235 - 245. https://doi.org/10.1006/gmip.1995.1022Li, S., Kang, X., Fang, L., Hu, J., Yin, H., 2017. Pixel-level image fusion: A survey of the state of the art. Information Fusion 33, 100 - 112. https://doi.org/10.1016/j.inffus.2016.05.004Li, S., Kwok, J. T., Wang, Y., 2001. Combination of images with diverse focuses using the spatial frequency. Information Fusion 2 (3), 169 - 176. https://doi.org/10.1016/S1566-2535(01)00038-0Li, S., Kwok, J. T., Wang, Y., 2002. Multifocus image fusion using artificial neural networks. Pattern Recognition Letters 23 (8), 985 - 997. https://doi.org/10.1016/S0167-8655(02)00029-6Li, S., Yang, B., 2008a. Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recognition Letters 29 (9), 1295-1301. https://doi.org/10.1016/j.patrec.2008.02.002Li, S., Yang, B., 2008b. Multifocus image fusion using region segmentation and spatial frequency. Image and Vision Computing 26 (7), 971 - 979. https://doi.org/10.1016/j.imavis.2007.10.012Li, X., He, M., Roux, M., August 2010. Multifocus image fusion based on redundant wavelet transform. Image Processing, IET 4 (4), 283-293. https://doi.org/10.1049/iet-ipr.2008.0259Liu, Y., Chen, X., Peng, H., Wang, Z., 2017a. Multi-focus image fusion with a deep convolutional neural network. Information Fusion 36, 191 - 207. https://doi.org/10.1016/j.inffus.2016.12.001Liu, Z., Chai, Y., Yin, H., Zhou, J., Zhu, Z., 2017b. A novel multi-focus image fusion approach based on image decomposition. Information Fusion 35, 102 - 116. https://doi.org/10.1016/j.inffus.2016.09.007Long, J., Shelhamer, E., Darrell, T., 2014. Fully convolutional networks for semantic segmentation. CoRR abs/1411.4038.Luo, X., Zhang, J., Dai, Q., 2012. A regional image fusion based on similarity characteristics. Signal Processing 92 (5), 1268 - 1280. https://doi.org/10.1016/j.sigpro.2011.11.021Ma, Y., Zhan, K.,Wang, Z., service), S. O., 2011. Applications of pulse-coupled neural networks.Malviya, A., Bhirud, S., Dec 2009. Wavelet based multi-focus image fusion. In: Methods and Models in Computer Science, 2009. ICM2CS 2009. Proceeding of International Conference on. pp. 1-6. https://doi.org/10.1109/ICM2CS.2009.5397990Nejati, M., Samavi, S., Shirani, S., 2015. Multi-focus image fusion using dictionary-based sparse representation. Information Fusion 25, 72 - 84. https://doi.org/10.1016/j.inffus.2014.10.004Orozco, R. I., 2013. Fusión de imágenes multifoco por medio de filtrado de regiones de alta y baja frecuencia. Master's thesis, División de Estudios de Postgrado. Facultad de Ingeniería Eléctrica. UMSNH, Morelia Michoacan Mexico.Pagidimarry, M., Babu, K. A., 2011. An all approach for multi-focus image fusion using neural network. Artificial Intelligent Systems and Machine Learning 3 (12), 732-739.Pajares, G., de la Cruz, J. M., 2004. A wavelet-based image fusion tutorial. Pattern Recognition 37 (9), 1855 - 1872. https://doi.org/10.1016/j.patcog.2004.03.010Piella, G., 2003. A general framework for multiresolution image fusion: from pixels to regions. Information Fusion 4 (4), 259 - 280. https://doi.org/10.1016/S1566-2535(03)00046-0Pramanik, S., Prusty, S., Bhattacharjee, D., Bhunre, P. K., 2013. A region-topixel based multi-sensor image fusion. Procedia Technology 10, 654 - 662. https://doi.org/10.1016/j.protcy.2013.12.407Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., Chen, Z., 2014. Magnetic resonance image reconstruction from undersampled measurements using a patchbased nonlocal operator. Medical Image Analysis 18 (6), 843 - 856, sparse Methods for Signal Reconstruction and Medical Image Analysis. https://doi.org/10.1016/j.media.2013.09.007Riaz, M., Park, S., Ahmad, M., Rasheed, W., Park, J., 2008. Generalized laplacian as focus measure. In: Bubak, M., van Albada, G., Dongarra, J., Sloot, P. (Eds.), Computational Science ICCS 2008. Vol. 5101 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 1013-1021. https://doi.org/10.1007/978-3-540-69384-0_106Rivera, M., Ocegueda, O., Marroquin, J., Dec 2007. Entropy-controlled quadratic markov measure field models for efficient image segmentation. Image Processing, IEEE Transactions on 16 (12), 3047-3057. https://doi.org/10.1109/TIP.2007.909384Sezan, M., Pavlovic, G., Tekalp, A., Erdem, A., Apr 1991. On modeling the focus blur in image restoration. In: Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on. pp. 2485-2488 vol.4. https://doi.org/10.1109/ICASSP.1991.150905Shah, P., Merchant, S. N., Desai, U. B., 2013. Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition. Signal, Image and Video Processing 7 (1), 95-109. https://doi.org/10.1007/s11760-011-0219-7Shi, W., Zhu, C., Tian, Y., Nichol, J., 2005. Wavelet-based image fusion and quality assessment. International Journal of Applied Earth Observation and Geoinformation 6 (3-4), 241 - 251. https://doi.org/10.1016/j.jag.2004.10.010Tian, J., Chen, L., Sept 2010. Multi-focus image fusion using wavelet-domain statistics. In: Image Processing (ICIP), 2010 17th IEEE International Conference on. pp. 1205-1208. https://doi.org/10.1109/ICIP.2010.5651791Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. pp. I-511-I-518 vol.1. https://doi.org/10.1109/CVPR.2001.990517Yang, Y., 2011. A novel fDWTg based multi-focus image fusion method. Procedia Engineering 24 (0), 177 - 181, international Conference on Advances in Engineering 2011.Yang, Y., Huang, S., Gao, J., Qian, Z., 2014. Multi-focus image fusion using an effective discrete wavelet transform based algorithm. Measurement Science Review 14 (2), 102 - 108. https://doi.org/10.2478/msr-2014-0014Yang, Y., Tong, S., Huang, S., Lin, P., 2015. Multifocus image fusion based on nsct and focused area detection. IEEE Sensors Journal 15 (5), 2824-2838. Zhang, B., Lu, X., Pei, H., Liu, H., Zhao, Y., Zhou, W., 2016a. Multi-focus image fusion algorithm based on focused region extraction. Neurocomputing 174, 733 - 748. https://doi.org/10.1016/j.neucom.2015.09.092Zhang, Q., long Guo, B., 2009. Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing 89 (7), 1334 - 1346. https://doi.org/10.1016/j.sigpro.2009.01.012Zhang, Y., Chen, L., Zhao, Z., Jia, J., 2016b. Multi-focus image fusion based on cartoon-texture image decomposition. Optik - International Journal for Light and Electron Optics 127 (3), 1291 - 1296. https://doi.org/10.1016/j.ijleo.2015.10.098Zhang, Z., Blum, R., Aug 1999. A categorization of multiscale-decompositionbased image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE 87 (8), 1315-1326. https://doi.org/10.1109/5.775414Zhao, H., Li, Q., Feng, H., 2008. Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map. Image and Vision Computing 26 (9), 1285 - 1295. https://doi.org/10.1016/j.imavis.2008.03.007Zhou, L., Ji, G., Shi, C., Feng, C., Nian, R., 2006. A Multi-focus Image Fusion Method Based on Image Information Features and the Artificial Neural Networks. Vol. 344. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 747-752. https://doi.org/10.1007/978-3-540-37256-1_91Zhou, Z., Li, S., Wang, B., 2014. Multi-scale weighted gradient-based fusion for multi-focus images. Information Fusion 20 (0), 60 - 72. https://doi.org/10.1016/j.inffus.2013.11.00