Image fusion and super-resolution with convolutional neural network

Communications in Computer and Information Science

Volumn 663, Issue , 2016, Pages 78-88

  Zhong, Jinying ^a   Yang, Bin ^a   Li, Yuehua ^a   Zhong, Fei ^a   Chen, Zhongze ^a  

^a UNIVERSITY OF SOUTH CHINA   (China)

Author keywords

Convolutional neural network; Image fusion; Super resolution

Indexed keywords

CONVOLUTION; INFRARED DEVICES; INFRARED RADIATION; MEDICAL IMAGING; NEURAL NETWORKS; OPTICAL RESOLVING POWER; PATTERN RECOGNITION;

CONVOLUTIONAL NEURAL NETWORK; MULTIFOCUS IMAGES; MULTIRESOLUTION CHARACTERISTICS; SIGNAL TRANSMISSION; SUPER RESOLUTION; SUPER RESOLUTION ALGORITHMS; SUPERRESOLUTION METHODS; TRADITIONAL PROCESSING;

IMAGE FUSION;

EID: 84994891108     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-981-10-3005-5_7     Document Type: Conference Paper

References (21)

1. Li, S.T., Kang, X.D., Hu, J.W.: Image fusion with guided filtering. J. IEEE Trans. Image Process. 22, 2864–2875 (2013)
2. Li, H., Li, L., Zhang, J.X.: Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering. J. Opt. Commun. 342, 1–11 (2015)
3. Chai, Y., Li, H.F., Guo, M.Y.: Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain. J. Opt. Commun. 284, 1146–1158 (2011)
4. Mirajkar, P.P., Sachin, D.R.: Image fusion based stationary wavelet transform. J. Int. J. Adv. Eng. Res. Stud. 2, 99–101 (2013)
5. Lewis, J.J., Callaghan, R.J., Nikolov, S.G.: Pixel- and region-based image fusion with complex wavelets. J. Inf. Fusion 8, 119–130 (2007)
6. Sravya, K., Govardhan, P., Goud, M.N.: Image fusion on multi focused images using NSCT. J. IJCSIT 5, 5393–5396 (2014)
7. Lui, S.-F., Wu, J.-Y., Mao, H.-S., Lien, J.-J.J.: Learning-based super-resolution system using single facial image and multi-resolution wavelet synthesis. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 96–105. Springer, Heidelberg (2007)
8. Liu, W.R., Li, S.T.: Image super-resolution based on hybrid multiresolution analysis. In: The First Asian Conference on Pattern Recognition, Beijing, China, pp. 307–310. IEEE press (2011)
9. Zhao, G., Zhang, K., Shao, W.: A study on NSCT based super-resolution reconstruction for infrared image. In: TENCON 2013 – 2013 IEEE Region 10 Conference, Xi’an, China, pp. 1–5. IEEE press (2013)
10. Lu, X., Li, X.: Multiresolution imaging. J. IEEE Trans. Cybern. 44, 149–160 (2014)
11. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 184–199. Springer, Heidelberg (2014)
12. Yang, J., Wright, J., Huang, T.S.: Image super-resolution via sparse representation. J. IEEE Trans. Image Process. 19, 2861–2873 (2010)
13. Ren, J.S., Xu, L., Yan, Q.: Shepard convolutional neural networks. In: Proceeding of 28th Annual Conference on Neural Information Processing Systems, Montreal, Canada. MIT press (2015)
14. Bevilacqua, M., Roumy, A., Guillemot, C.: Low-complexity single-image super-resolutionbased on nonnegative neighbor embedding. In: Proceedings of the 23rd British Machine Vision Conference (BMVC), pp. 135.1–135.10. BMVC press, Guidford (2012)
15. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: IEEE Conference on Computer Vision and Pattern Recognition, California, USA, pp. 275–282. IEEE Computer Society (2010)
16. Jia, K., Wang, X., Tang, X.: Image transformation based on learning dictionaries across image spaces. J. IEEE Trans. Pattern Anal. Mach. Intell. 35, 367–380 (2013)
17. Timofte, R., De, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International Conference on Computer Vision, Sydney, Australia, pp. 1920–1927. IEEE press (2013)
18. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
19. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. J. Nat. 323, 533–536 (1986)
20. Piella, G., Heijmans, H.: A new quality metric for image fusion. In: Proceedings of the IEEE International Conference on Image Processing, Barcelona, Spain, pp. 173–176. IEEE press (2003)
21. Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. J. Electron. Lett. 36, 308–309 (2000)