• Acta Physica Sinica
  • Vol. 69, Issue 6, 064201-1 (2020)
Zheng-De Xia1, Na Song1, Bin Liu2、*, Jin-Xiao Pan2, Wen-Min Yan3, and Zi-Hui Shao4
Author Affiliations
  • 1Shanxi Key Laboratory of Signal Capturing & Processing, School of Science, North University of China, Taiyuan 030051, China
  • 2Shanxi Key Laboratory of Signal Capturing & Processing, School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
  • 3Science and Technology on Transient Impact Laboratory, Beijing 102202, China
  • 4Unit 32178, Beijing 100220, China
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    DOI: 10.7498/aps.69.20191621 Cite this Article
    Zheng-De Xia, Na Song, Bin Liu, Jin-Xiao Pan, Wen-Min Yan, Zi-Hui Shao. Dense light field reconstruction algorithm based on dictionary learning[J]. Acta Physica Sinica, 2020, 69(6): 064201-1 Copy Citation Text show less
    Algorithm workflow.
    Fig. 1. Algorithm workflow.
    Light field overcomplete dictionary.
    Fig. 2. Light field overcomplete dictionary.
    Performance of reconstructed image: (a) Performance in sparsity, pixels = 256 × 256; (b) performance in sparsity, pixels = 512 × 512; (c) PSNR in different resolution; (d) performance in redundancy, pixels = 256 × 256.
    Fig. 3. Performance of reconstructed image: (a) Performance in sparsity, pixels = 256 × 256; (b) performance in sparsity, pixels = 512 × 512; (c) PSNR in different resolution; (d) performance in redundancy, pixels = 256 × 256.
    Image reconstruction in different sparsity and redundancy: (a) K = 16, N = 256; (b) K = 34, N = 1024
    Fig. 4. Image reconstruction in different sparsity and redundancy: (a) K = 16, N = 256; (b) K = 34, N = 1024
    Dense reconstruction of light field with occluded targets: (a) Dense light field; (b), (e) reference images; (c), (d) reconstructed virtual images of view 2 and view 5; (g), (h) target images; (f), (i) residual images.
    Fig. 5. Dense reconstruction of light field with occluded targets: (a) Dense light field; (b), (e) reference images; (c), (d) reconstructed virtual images of view 2 and view 5; (g), (h) target images; (f), (i) residual images.
    Dense reconstruction of light field: (a) Reconstructed image for proposed algorithm; (b) reconstructed image for DIBR; (c) target image; (d) residual image; (e) dense light field.
    Fig. 6. Dense reconstruction of light field: (a) Reconstructed image for proposed algorithm; (b) reconstructed image for DIBR; (c) target image; (d) residual image; (e) dense light field.
    Sparse parameter (K), Redundancy parameter (N) MSEPSNR/dBSSIMTime/s
    K = 16, N = 256 54.421530.77310.88601266.08
    K = 34, N = 1024 49.004431.22850.886514306.55
    Table 1. Performance of image reconstruction in different sparsity and redundancy
    SenseTableBicycletownBoardgamesrosemaryVinylbicycle*
    * 稀疏度K = 34, 冗余度N = 1024.
    MSE21.212454.421525.800553.924418.895022.475649.0044
    PSNR/dB34.864930.773134.014530.812935.367334.613731.2285
    SSIM0.93230.88600.94740.93410.96990.94210.8865
    Table 2. Dense reconstruction of light field in different scenes.
    Zheng-De Xia, Na Song, Bin Liu, Jin-Xiao Pan, Wen-Min Yan, Zi-Hui Shao. Dense light field reconstruction algorithm based on dictionary learning[J]. Acta Physica Sinica, 2020, 69(6): 064201-1
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