Abstract
Capturing all-in-focus images with 3D scenes is typically a challenging task due to depth of field limitations, and various multi-focus image fusion methods have been employed to generate all-in-focus images. However, existing methods have difficulty simultaneously achieving real-time and superior fusion performance. In this paper, we propose a region- and pixel-based method that can recognize the focus and defocus regions or pixels by the neighborhood information in the source images. The proposed method can obtain satisfactory fusion results and achieve improved real-time performance. First, a convolutional neural network (CNN)-based classifier generates a coarse region-based trimap quickly, which contains focus, defocus and boundary regions. Then, precise fine-tuning is implemented at the boundary regions to address the boundary pixels that are difficult to discriminate by existing methods. Based on a public database, a high-quality dataset is constructed that provides abundant precise pixel-level labels, so that the proposed method can accurately classify the regions and pixels without artifacts. Furthermore, an image interpolation method called NEAREST_Gaussian is proposed to improve the recognition ability at the boundary. Experimental results show that the proposed method outperforms other state-of-the-art methods in visual perception and object metrics. Additionally, the proposed method has 80% improved to the conventional CNN-based methods.
Similar content being viewed by others
Availability of data and material
Not applicable.
References
Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Inf Fusion 36:191–207. https://doi.org/10.1016/j.inffus.2016.12.001
Burt PJ, Adelson EH (1987) The laplacian pyramid as a compact image code. Readings Comput Vision 31(4):671–679. https://doi.org/10.1016/b978-0-08-051581-6.50065-9
Lewis JJ, O’Callaghan RJ, Nikolov SG, Bull DR, Canagarajah N (2007) Pixel- and region-based image fusion with complex wavelets. Inf Fusion 8(2):119–130. https://doi.org/10.1016/j.inffus.2005.09.006
Liu Y, Jin J, Wang Q, Shen Y, Dong X (2014) Region level based multi-focus image fusion using quaternion wavelet and normalized cut. Signal Process 97:9–30. https://doi.org/10.1016/j.sigpro.2013.10.010
Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Inf Fusion 8(2):143–156. https://doi.org/10.1016/j.inffus.2006.02.001
Bavirisetti DP, Dhuli R (2018) Multi-focus image fusion using multi-scale image decomposition and saliency detection. Ain Shams Eng J 9(4):1103–1117. https://doi.org/10.1016/j.asej.2016.06.011
Mitianoudis N, Stathaki T (2007) Pixel-based and region-based image fusion schemes using ICA bases. Inf Fusion 8(2):131–142. https://doi.org/10.1016/j.inffus.2005.09.001
Bin Y, Shutao L (2010) Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas 59(4):884–892. https://doi.org/10.1109/tim.2009.2026612
Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Inf Fusion 24:147–164. https://doi.org/10.1016/j.inffus.2014.09.004
Huang W, Jing Z (2007) Evaluation of focus measures in multi-focus image fusion. Pattern Recog Lett 28(4):493–500. https://doi.org/10.1016/j.patrec.2006.09.005
De I, Chanda B (2013) Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure. Inf Fusion 14(2):136–146. https://doi.org/10.1016/j.inffus.2012.01.007
Duan J, Chen L, Philip Chen CL (2016) Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering. Appl Opt 55(36):10352. https://doi.org/10.1364/ao.55.010352
Li M, Cai W, Tan Z (2006) A region-based multi-sensor image fusion scheme using pulse-coupled neural network. Pattern Recog Lett 27(16):1948–1956. https://doi.org/10.1016/j.patrec.2006.05.004
Li S, Kang X, Hu J, Yang B (2013) Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion 14(2):147–162. https://doi.org/10.1016/j.inffus.2011.07.001
Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875. https://doi.org/10.1109/tip.2013.2244222
Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense SIFT. Inf Fusion 23:139–155. https://doi.org/10.1016/j.inffus.2014.05.004
Ma H, Zhang J, Liu S, Liao Q (2019) Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network. 2019 IEEE International Conference on Multimedia and Expo (ICME). https://doi.org/10.1109/icme.2019.00201
Tang H, Xiao B, Li W, Wang G (2018) Pixel convolutional neural network for multi-focus image fusion. Inf Sci (N Y) 433-434:125–141. https://doi.org/10.1016/j.ins.2017.12.043
Du C, Gao S (2017) Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network. IEEE Access 5:15750–15761. https://doi.org/10.1109/access.2017.2735019
Zhang Q, Guo B-L (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334–1346. https://doi.org/10.1016/j.sigpro.2009.01.012
Rhemann C, Rother C, Wang J, Gelautz M, Kohli P, Rott P (2009) A perceptually motivated online benchmark for image matting. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition pp 1826-1833. https://doi.org/10.1109/cvpr.2009.5206503
Lan R, Lu H, Zhou Y, Liu Z, Luo X (2019) An lbp encoding scheme jointly using quaternionic representation and angular information. Neural Comput & Applic 32:4317–4323. https://doi.org/10.1007/s00521-018-03968-y
Xu X, Lu H, Song J, Yang Y, Shen HT, Li X (2019) Ternary adversarial networks with self-supervision for zero-shot cross-modal retrieval. IEEE transactions on. Cybernetics PP(99):1–14. https://doi.org/10.1109/TCYB.2019.2928180
Lan R, Zhou Y, Liu Z, Luo X (2018). Prior knowledge-based probabilistic collaborative representation for visual recognition. IEEE transactions on cybernetics, 1-11. https://doi.org/10.1109/tcyb.2018.2880290
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/tpami.2016.2577031
Chen Y, Yang T, Zhang X, Meng G, Pan C, Sun J (2019) Detnas: neural architecture search on object detection. arXiv preprint:arXiv:1903.10979. https://arxiv.org/abs/1903.10979. Accessed 9 April 2020
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. MICCAI 2015, Lecture notes in computer science, vol, vol 9351. Springer, Cham, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
Li H, Xiong P, Fan H, Sun J (2019) Dfanet: Deep feature aggregation for real-time semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp 9522–9531. https://doi.org/10.1109/cvpr.2019.00975
Zhang W, Dong L, Pan X, Zhou J, Qin L, Xu W (2019) Single image defogging based on multi-channel convolutional MSRCR. IEEE Access 7:72492–72504. https://doi.org/10.1109/access.2019.2920403
Lan R, Sun L, Liu Z, Lu H, Luo X (2020) Madnet: a fast and lightweight network for single-image super resolution. IEEE Trans Cybernetics PP(99):1–11. https://doi.org/10.1109/TCYB.2020.2970104
Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE internet things J PP(99):1–1. https://doi.org/10.1109/JIOT.2017.2737479
Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mobile Networks Appl 23(2):368–375. https://doi.org/10.1007/s11036-017-0932-8
Chen X, Fang H, Lin T-Y, Vedantam R, Gupta S, Dollár P, Zitnick CL (2015) Microsoft COCO captions: data collection and evaluation server. arXiv preprint:arXiv:1504.00325. https://arxiv.org/abs/1504.00325. Accessed 9 April 2020
Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision pp 2980-2988. https://doi.org/10.1109/iccv.2017.324
Zhou Z, Li S, Wang B (2014) Multi-scale weighted gradient-based fusion for multi-focus images. Inf Fusion 20:60–72. https://doi.org/10.1016/j.inffus.2013.11.005
Zhang Y, Bai X, Wang T (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inf Fusion 35:81–101 doi.org/10.1016/j.inffus.2016.09.006
Liu Z, Blasch E, Xue Z, Zhao J, Laganiere R, Wu W (2012) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study. IEEE Trans Pattern Anal Mach Intell 34(1):94–109. https://doi.org/10.1109/tpami.2011.109
Johnson DH (2006) Signal-to-noise ratio. Scholarpedia 1(12):2088. https://doi.org/10.4249/scholarpedia.2088
Zhao Y, Jia R, Shi P (2016) A novel combination method for conflicting evidence based on inconsistent measurements. Inf Sci (N Y) 367-368:125–142. https://doi.org/10.1016/j.ins.2016.05.039
Zhao W, Lu H, Wang D (2017) Multisensor image fusion and enhancement in spectral total variation domain. IEEE Trans Multimed PP(99):1-1. https://doi.org/10.1109/TMM.2017.2760100
Lu H, Li B, Zhu J, LiY SS (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurrency Comput Pract Exp 29(6). https://doi.org/10.1002/cpe.3927
Acknowledgments
National Key R&D Program (2018AAA0102600).
Funding
National Key R&D Program (2018AAA0102600).
Author information
Authors and Affiliations
Contributions
Not applicable.
Corresponding author
Ethics declarations
Conflict of interest
The authors declared that they have no conflicts of interest.
Code availability
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhao, W., Yang, H., Wang, J. et al. Region- and Pixel-Level Multi-Focus Image Fusion through Convolutional Neural Networks. Mobile Netw Appl 26, 40–56 (2021). https://doi.org/10.1007/s11036-020-01719-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-020-01719-9