• Opto-Electronic Engineering
  • Vol. 47, Issue 12, 200007 (2020)
Zhao Yuanyuan and Shi Shengxian*
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.12086/oee.2020.200007 Cite this Article
    Zhao Yuanyuan, Shi Shengxian. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 2020, 47(12): 200007 Copy Citation Text show less
    References

    [1] Lippmann G. épreuves réversibles donnant la sensation du relief[J]. Journal de Physique Théorique et Appliquée, 1908, 7(1): 821.825.

    [2] Adelson E H, Wang J Y A. Single lens stereo with a plenoptic camera[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 99.106.

    [3] Ng R, Levoy M, Brédif M, et al. Light field photography with a hand-held plenoptic camera[R]. Stanford Tech Report CTSR 2005-02, 2005.

    [4] Tan Z P, Johnson K, Clifford C, et al. Development of a modular, high-speed plenoptic-camera for 3D flow-measurement[J]. Op-tics Express, 2019, 27(9): 13400.13415.

    [5] Fahringer T W, Lynch K P, Thurow B S. Volumetric particle im-age velocimetry with a single plenoptic camera[J]. Measurement Science and Technology, 2015, 26(11): 115201.

    [6] ShiSX, DingJF, New T H, et al. Volumetric calibration en-hancements for single-camera light-field PIV[J]. Experiments in Fluids, 2019, 60(1): 21.

    [7] ShiSX, DingJF, New T H, et al. Light-field camera-based 3D volumetric particle image velocimetry with dense ray tracing re-construction technique[J]. Experiments in Fluids, 2017, 58(7): 78.

    [8] Shi S X,Wang J H,DingJF, et al. Parametric study on light field volumetric particle image velocimetry[J]. Flow Measurement and Instrumentation, 2016, 49: 70.88.

    [9] Sun J, Xu C L, Zhang B, et al. Three-dimensional temperature field measurement of flame using a single light field camera[J]. Optics Express, 2016, 24(2): 1118.1132.

    [10] Shi S X,Xu S M,Zhao Z, et al. 3D surface pressure measure-ment with single light-field camera and pressure-sensitive paint[J]. Experiments in Fluids, 2018, 59(5): 79.

    [11] DingJF,LiHT,MaHX, et al. A novel light field imaging based 3D geometry measurement technique for turbomachinery blades[J]. Measurement Science and Technology, 2019, 30(11): 115901.

    [12] Cheng Z, Xiong Z W, Chen C, et al. Light field super-resolution: a benchmark[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, 2019.

    [13] Lim J, Ok H, Park B, et al. Improving the spatail resolution based on 4D light field data[C]//Proceedings of the 16th IEEE Interna-tional Conference on Image Processing, Cairo, Egypt, 2009, 2: 1173.1176.

    [14] Georgiev T, Chunev G, Lumsdaine A. Superresolution with the focused plenoptic camera[J]. Proceedings of SPIE, 2011, 7873: 78730X.

    [15] Bishop T E, Favaro P. The light field camera: extended depth of field, aliasing, and superresolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 972.986.

    [16] Rossi M, Frossard P. Graph-based light field su-per-resolution[C]//Proceedings of the IEEE 19th International Workshop on Multimedia Signal Processing, Luton, UK, 2017: 1.6.

    [17] Alain M, Smolic A. Light field super-resolution via LFBM5D sparse coding[C]//Proceedings of the 25th IEEE International Conference on Image Processing, Athens, Greece, 2018: 1.5.

    [18] Egiazarian K, Katkovnik V. Single image super-resolution via BM3D sparse coding[C]//Proceedings of the 23rd European Signal Processing Conference, Nice, France, 2015: 2849.2853.

    [19] Alain M, Smolic A. Light field denoising by sparse 5D transform domain collaborative filtering[C]//Proceedings of the IEEE 19th International Workshop on Multimedia Signal Processing, Luton, UK, 2017: 1.6.

    [20] Yoon Y, Jeon H G, Yoo D, et al. Learning a deep convolutional network for light-field image super-resolution[C]//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop, Santiago, Chile, 2015: 57.65.

    [21] Wang Y L, Liu F, Zhang K B, et al. LFNet: a novel bidirectional recurrent convolutional neural network for light-field image su-per-resolution[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4274.4286.

    [22] Zhang S, Lin Y F, Sheng H. Residual networks for light field image super-resolution[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019: 11046.11055.

    [23] Wang L G, Wang Y Q, Liang Z F, et al. Learning parallax atten-tion for stereo image super-resolution[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recog-nition, Long Beach, CA, USA, 2019: 12250.12259.

    [24] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmenta-tion[C]//Proceedings of the European Conference on Computer Vision, Glasgow, United Kingdom, 2018: 801.818.

    [26] Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolu-tional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1874.1883.

    [28] Wanner S, Meister S, Goldluecke B. Datasets and benchmarks for densely sampled 4D light fields[M]//Bronstein M, Favre J, Hormann K. Vision, Modeling & Visualization, Lugano, Switzer-land: The Eurographics Association, 2013: 225.226.

    [29] Honauer K, Johannsen O, Kondermann D, et al. A dataset and evaluation methodology for depth estimation on 4D light fields[C]//Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan, China, 2016: 19.34.

    [30] Raj S A, Lowney M, Shah R, et al. Stanford lytro light field arc-hive[EB/OL]. http://lightfields.stanford.edu/LF2016.html. 2016.

    [31] Rerabek M, Ebrahimi T. New light field image data-set[C]//Proceedings of the 8th International Conference on Quality of Multimedia Experience, Lisbon, Portugal, 2016.

    [32] Chu X X, Zhang B, Ma H L, et al. Fast, accurate and lightweight super-resolution with neural architecture search[Z]. arXiv: 1901.07261, 2019.

    [33] Kingma D P, Ba L J. Adam: a method for stochastic optimiza-tion[C]//Proceedings of the International Conference on Learning Representations, San Diego,America, 2015.

    [34] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the 13th Inter-national Conference on Artificial Intelligence and Statistics, Sar-dinia, Italy, 2010: 249.256.

    [35] Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//British Machine Vision Conference, Guildford, UK, 2012.

    [36] Chen J, Hou J H, Ni Y, et al. Accurate light field depth estimation with superpixel regularization over partially occluded regions[J]. IEEE Transactions on Image Processing, 2018, 27(10): 4889.4900.

    CLP Journals

    [1] Ma Shuai, Wang Ning, Zhu Licheng, Wang Shuai, Yang Ping, Xu Bing. Light field depth estimation using weighted side window angular coherence[J]. Opto-Electronic Engineering, 2021, 48(12): 210405

    Zhao Yuanyuan, Shi Shengxian. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 2020, 47(12): 200007
    Download Citation