级联金字塔结构的深度图超分辨率重建

付绪文, 张旭东, 张骏, 等. 级联金字塔结构的深度图超分辨率重建[J]. 光电工程, 2019, 46(11): 180587. doi: 10.12086/oee.2019.180587
引用本文: 付绪文, 张旭东, 张骏, 等. 级联金字塔结构的深度图超分辨率重建[J]. 光电工程, 2019, 46(11): 180587. doi: 10.12086/oee.2019.180587
Fu Xuwen, Zhang Xudong, Zhang Jun, et al. Depth map super-resolution with cascaded pyramid structure[J]. Opto-Electronic Engineering, 2019, 46(11): 180587. doi: 10.12086/oee.2019.180587
Citation: Fu Xuwen, Zhang Xudong, Zhang Jun, et al. Depth map super-resolution with cascaded pyramid structure[J]. Opto-Electronic Engineering, 2019, 46(11): 180587. doi: 10.12086/oee.2019.180587

级联金字塔结构的深度图超分辨率重建

  • 基金项目:
    国家自然科学基金资助项目(61471154,61876057)
详细信息
    作者简介:
    通讯作者: 张旭东(1966-),男,博士,教授,主要从事智能信息处理、机器视觉的研究。E-mail: xudong@hfut.edu.cn
  • 中图分类号: TB872

Depth map super-resolution with cascaded pyramid structure

  • Fund Project: Supported by National Natural Science Foundation of China (61471154, 61876057)
More Information
  • 由于成像设备的限制,深度图往往分辨率较低。对低分辨率深度图进行上采样时,通常会造成深度图的边缘模糊。当上采样因子较大时,这种问题尤为明显。本文提出金字塔密集残差网络,实现深度图超分辨率重建。整个网络以残差网络为主框架,采用级联的金字塔结构对深度图分阶段上采样。在每一阶段,采用简化的密集连接块获取图像的高频残差信息,尤其是底层的边缘信息,同时残差结构中的跳跃连接分支获取图像的低频信息。网络直接以原始低分辨率深度图作为输入,以亚像素卷积层进行上采样操作,减少了运算复杂度。实验结果表明,该方法有效地解决了图像深度边缘的模糊问题,在定性和定量评价上优于现有方法。

  • Overview: With the development of science and technology, depth information is gradually applied to various fields of society, such as face recognition, virtual reality and so on. However, due to the limitation of hardware conditions such as sensors, the resolution of the depth images is too low to meet the requirements of the reality. Depth map super- resolution has been an important research area in the field of computer vision. Early methods adopt interpolation, filtering. Although these methods are simple and fast in running, the clues used by these methods are limited and the results are not ideal. The details of the depth map is lost, especially when the upsampling factor is large. To address the above issue, intensity images are used to guide the depth map super-resolution. But when the local structures in the guidance and depth images are not consistent, these techniques may cause the over-texture transferring problem. At present, convolutional neural networks are widely used in computer vision because of its powerful feature representation ability. Several models based on convolutional neural networks have achieved great success in single image super-resolution. To solve the problems of edge blurring and over-texture transferring in depth super-resolution, we propose a new framework to achieve single depth image super-resolution based on the pyramid dense residual network (PDRN). The PDRN directly uses the low-resolution depth image as the input of the network and doesn't require the pre-processing. This can reduce the computational complexity and avoid the additional error. The network takes the residual network as the backbone model and adopts the cascaded pyramid structure for phased upsampling. The result of each pyramid stage is used as the input of the next stage. At each pyramid stage, the modified dense block is used to acquire high frequency residual, and subpixel convolution layer is used for upsampling. The dense block enhances transmission of feature and reduces information loss. Therefore, the network can reconstruct high resolution depth maps using different levels of features. The residual structure is used to shorten the time of convergence and improve the accuracy. In addition, the network adopts the Charbonnier loss function to train the network. By constraining the results of each stage, the training network can get more stable results. Experiments show that the proposed network can avoid edge blurring, detail loss and over-texture transferring in depth map super-resolution. Extensive evaluations on several datasets indicate that the proposed method obtains better performance comparing to other state-of-the-art methods.

  • 加载中
  • 图 1  整体网络结构。该图是上采样因子为4时的结构图,包含2个阶段。每个阶段上采样因子为2,一个阶段的输出作为下一阶段的输入

    Figure 1.  The network structure. It is for upscale factor 4 that contains two stages. The upscale factor for each stage is 2 and the output of one stage is used as the input of the next stage

    图 2  金字塔密集残差网络(PDRN)单阶段网络结构,上采样因子为2。该结构包含四种运算:特征提取、非线性映射、融合、上采样

    Figure 2.  The single stage of the pyramid dense residual network (PDRN) for upscale factor 2. It contains four operations: feature extraction, non-linear, fusion and upsampling

    图 3  重建结果图及频谱分析图。(a)最终结果图及对应的谱分析图;(b)子带残差结果图及其频谱图;(c)跳跃连接分支结果图及其频谱图

    Figure 3.  Reconstruction results and spectrum analysis. (a) Final result and corresponding spectrum analysis; (b) Residual result and its spectrum analysis; (c) Skip connection result and its spectrum analysis

    图 4  (a) 常见的密集连接块;(b)本文采用的密集连接块,通过跳跃连接,特征图的数量由32增加到256

    Figure 4.  (a) The original dense block; (b) Our dense block that the number of feature maps is changed from 32 to 256 through three skip connections

    图 5  上采样为4的重建结果。(a)真实深度图;(b)最近邻插值;(c)双三次插值;(d) PDN;(e)本文方法

    Figure 5.  Reconstruction results for scale factor 4. (a) Ground truth; (b) Nearest; (c) Bicubic; (d) PDN; (e) PDRN

    图 6  收敛曲线

    Figure 6.  Convergent curve

    图 7  上采样深度图。(a)真实深度图;(b) TGV[3];(c) AP[27];(d) MS-Net[2];(e) LapSRN[18];(f)本文方法

    Figure 7.  Upsampling depth map. (a) Ground truth; (b) TGV[3]; (c) AP[27]; (d) MS-Net[2]; (e) LapSRN[18]; (f) Ours

    图 8  上采样深度图。(a)真实深度图;(b) Bicubic;(c) Xie[39];(d) MS-Net[2];(e) LapSRN[18];(f)本文方法

    Figure 8.  Upsampling depth map. (a) Ground truth; (b) Bicubic; (c) Xie[39]; (d) MS-Net[2]; (e) LapSRN[18]; (f) Ours

    表 1  有无残差结构在数据集Laser Scan的定量评价(RMSE/SSIM)

    Table 1.  Quantitative comparison (in RMSE and SSIM) on dataset Laser Scan

    Scan1 Scan2 Scan3
    Nearest 5.401/0.9820 8.567/0.9573 13.02/0.9176 4.251/0.9841 6.701/0.9630 9.992/0.9301 4.540/0.9842 8.112/0.9560 11.15/0.9218
    Bicubic 4.215/0.9864 6.496/0.9689 10.09/0.9377 3.477/0.9873 5.234/0.9734 7.825/0.9505 4.063/0.9865 6.415/0.9658 8.930/0.9385
    PDN 2.480/0.9917 3.754/0.9873 5.542/0.9791 2.106/0.9921 3.139/0.9880 4.455/0.9809 1.861/0.9951 2.889/0.9920 4.196/0.9866
    PDRN 2.170/0.9923 3.612/0.9878 5.391/0.9797 1.800/0.9927 3.041/0.9884 4.355/0.9815 1.303/0.9962 2.695/0.9926 4.006/0.9876
    下载: 导出CSV

    表 2  数据集A上重建结果的定量评价(RMSE)

    Table 2.  Quantitative comparison (in RMSE) on dataset A

    Art Books Moebius
    16× 16× 16×
    Bicubic 2.5837 3.8565 5.5282 8.3759 1.0321 1.5794 2.2693 3.3652 0.9304 1.4006 2.0581 2.9702
    MRF[24] 3.0192 3.6693 5.3349 8.3815 1.1976 1.5359 2.2026 3.4137 1.1665 1.4104 1.9905 2.9874
    Guided[4] 2.8449 3.6686 4.8252 7.5978 1.1607 1.5646 2.0843 3.1856 1.0748 1.4029 1.8258 2.7592
    Edge[25] 2.7502 3.4110 4.0320 6.0719 1.0611 1.5216 1.9754 2.7581 1.0501 1.3372 1.7863 2.3511
    TGV[3] 2.9216 3.6474 4.6034 6.8204 1.2818 1.5841 1.9692 2.9503 1.1136 1.4302 1.8397 2.5216
    AP[27] 1.7909 2.8513 3.6943 5.9113 1.3280 1.5297 1.8394 2.9187 0.8704 1.0311 1.5505 2.5728
    SRCNN[13] 1.1338 2.0175 3.8293 7.2717 0.5231 0.9356 1.7268 3.1006 0.5374 0.9132 1.5790 2.6896
    VDSR[15] 1.3242 2.0710 3.2433 6.6622 0.4883 0.8296 1.2878 2.1433 0.5441 0.8341 1.2952 2.1590
    MS-Net[2] 0.8131 1.6352 2.7697 5.8040 0.4180 0.7404 1.0770 1.8165 0.4133 0.7448 1.1384 1.9151
    LapSRN[18] 0.7644 2.3796 3.3389 6.1110 0.4443 0.9423 1.3430 1.9523 0.4300 0.9382 1.3388 2.0450
    PDRN 0.6233 1.6634 2.6943 5.8083 0.3875 0.7313 1.0668 1.7323 0.3723 0.7347 1.0767 1.8567
    下载: 导出CSV

    表 3  数据集A上重建结果的定量评价(SSIM)

    Table 3.  Quantitative comparison (in SSIM) on dataset A

    Art Books Moebius
    16× 16× 16×
    Bicubic 0.9868 0.9679 0.9433 0.9254 0.9956 0.9900 0.9835 0.9789 0.9950 0.9888 0.9811 0.9761
    MRF[24] 0.9833 0.9749 0.9570 0.9371 0.9945 0.9916 0.9865 0.9811 0.9929 0.9901 0.9846 0.9792
    Guided[4] 0.9830 0.9710 0.9579 0.9476 0.9945 0.9906 0.9865 0.9833 0.9934 0.9894 0.9853 0.9812
    Edge[25] 0.9870 0.9797 0.9725 0.9605 0.9956 0.9918 0.9877 0.9846 0.9945 0.9910 0.9868 0.9840
    TGV[3] 0.9858 0.9783 0.9668 0.9535 0.9945 0.9919 0.9886 0.9843 0.9941 0.9907 0.9860 0.9819
    AP[27] 0.9952 0.9871 0.9798 0.9586 0.9961 0.9942 0.9909 0.9849 0.9972 0.9950 0.9906 0.9833
    VDSR[15] 0.9964 0.9916 0.9801 0.9457 0.9985 0.9966 0.9932 0.9866 0.9980 0.9958 0.9913 0.9823
    MS-Net[2] 0.9983 0.9941 0.9851 0.9557 0.9990 0.9973 0.9949 0.9894 0.9988 0.9965 0.9928 0.9851
    LapSRN[18] 0.9984 0.9888 0.9791 0.9584 0.9986 0.9958 0.9931 0.9891 0.9986 0.9949 0.9912 0.9852
    PDRN 0.9988 0.9945 0.9871 0.9601 0.9990 0.9971 0.9949 0.9898 0.9988 0.9965 0.9935 0.9863
    下载: 导出CSV

    表 4  数据集B上重建结果的定量评价(RMSE)

    Table 4.  Quantitative comparison (in RMSE) on dataset B

    Dolls Laundry Reindeer
    16× 16× 16×
    Bicubic 0.9433 1.3356 1.8811 2.6451 1.6142 2.4077 3.4520 5.0923 1.9382 2.8086 3.9857 5.8591
    Edge[25] 0.9713 1.3217 1.7750 2.4341 1.5525 2.1316 2.7698 4.1581 2.2674 2.4067 2.9873 4.2941
    TGV[3] 1.1467 1.3869 1.8854 3.5878 1.9886 2.5115 3.7570 6.4066 2.4068 2.7115 3.7887 7.2711
    AP[27] 1.1893 1.3888 1.6783 2.3456 1.7154 2.2553 2.8478 4.6564 1.8026 2.4309 2.9491 4.0877
    SRCNN[13] 0.5814 0.9467 1.5185 2.4452 0.6353 1.1761 2.4306 4.5795 0.7658 1.4997 2.8643 5.2491
    VDSR[15] 0.6308 0.8966 1.3148 2.0912 0.7249 1.1974 1.8392 3.2061 1.0051 1.5058 2.2814 4.1759
    MS-Net[2] 0.4813 0.7835 1.2049 1.8627 0.4749 0.8842 1.6279 3.4353 0.5563 1.1068 1.9719 3.9215
    LapSRN[18] 0.4942 1.0111 1.4194 1.9954 0.4617 1.3552 1.9314 2.5137 0.5075 1.7402 2.4538 3.0737
    PDRN 0.4418 0.8317 1.1916 1.8230 0.3897 0.9240 1.4207 2.2084 0.4149 1.1047 1.7392 2.6655
    下载: 导出CSV

    表 5  数据集B上重建结果的定量评价(SSIM)

    Table 5.  Quantitative comparison (in SSIM) on dataset B

    Dolls Laundry Reindeer
    16× 16× 16×
    Bicubic 0.9948 0.9893 0.9827 0.9783 0.9926 0.9833 0.9717 0.9635 0.9930 0.9846 0.9737 0.9642
    Edge[25] 0.9950 0.9911 0.9876 0.9849 0.9938 0.9892 0.9850 0.9774 0.9897 0.9902 0.9863 0.9819
    TGV[3] 0.9934 0.9902 0.9853 0.9770 0.9882 0.9820 0.9714 0.9551 0.9910 0.9874 0.9798 0.9690
    AP[27] 0.9925 0.9901 0.9876 0.9842 0.9942 0.9905 0.9876 0.9757 0.9931 0.9891 0.9860 0.9794
    VDSR[15] 0.9976 0.9953 0.9910 0.9457 0.9979 0.9835 0.9878 0.9762 0.9977 0.9953 0.9906 0.9773
    MS-Net[2] 0.9987 0.9964 0.9921 0.9851 0.9989 0.9965 0.9899 0.9751 0.9989 0.9968 0.9929 0.9814
    LapSRN[18] 0.9982 0.9941 0.9901 0.9851 0.9988 0.9942 0.9892 0.9858 0.9987 0.9938 0.9896 0.9868
    PDRN 0.9985 0.9958 0.9922 0.9860 0.9990 0.9970 0.9937 0.9872 0.9990 0.9968 0.9942 0.9887
    下载: 导出CSV

    表 6  数据集C上重建结果的定量评价(RMSE/SSIM)

    Table 6.  Quantitative comparison (in RMSE/SSIM) on dataset C

    Cones Teddy Tsukuba
    Bicubic 2.5422/0.9813 3.8666/0.9583 1.9610/0.9844 2.8583/0.9665 5.8201/0.9694 8.5638/0.9277
    Edge[25] 2.8497/0.9699 6.5447/0.9420 2.1850/0.9767 4.3366/0.9553 6.8869/0.9320 12.123/0.8981
    Ferstl[38] 2.1850/0.9866 3.4977/0.9645 1.6941/0.9884 2.5966/0.9716 5.3252/0.9766 7.5356/0.9413
    Xie[39] 2.7338/0.9633 4.4087/0.9319 2.4911/0.9625 3.2768/0.9331 6.3534/0.9464 9.7765/0.8822
    Song[40] 1.4356/0.9989 2.9789/0.9783 1.1974/0.9918 1.8006/0.9831 2.9841/0.9905 6.1422/0.9666
    SRCNN[13] 1.4842/0.9965 3.5856/0.9672 1.1702/0.9923 1.9857/0.9820 3.2753/0.9879 7.9391/0.9587
    VDSR[15] 1.7150/0.9917 2.9808/0.9797 1.2203/0.9925 1.8591/0.9836 3.7684/0.9896 5.9175/0.9686
    MS-Net[2] 1.1005/0.9951 2.7659/0.9817 0.8204/0.9953 1.5283/0.9865 2.4536/0.9934 4.9927/0.9740
    LapSRN[18] 1.0182/0.9958 3.1994/0.9755 0.8570/0.9951 2.0820/0.9802 2.0822/0.9960 6.2983/0.9649
    PDRN 0.8556/0.9963 2.6049/0.9837 0.7359/0.9959 1.6421/0.9860 1.8128/0.9974 4.9136/0.9798
    下载: 导出CSV

    表 7  数据集D上重建结果的定量评价(RMSE/SSIM)

    Table 7.  Quantitative comparison (in RMSE/SSIM) on dataset D

    Scan1 Scan2 Scan3
    Bicubic 4.2153/0.9864 6.4958/0.9689 3.4766/0.9873 5.2335/0.9734 4.0629/0.9865 6.4149/0.9658
    Xie[39] - 9.1935/0.9781 - 7.4148/0.9791 - 8.9093/0.9680
    VDSR[15] 2.7391/0.9911 3.9732/0.9865 2.2883/0.9919 3.2704/0.9878 1.4128/0.9960 3.0617/0.9912
    MS-Net[2] 2.7502/0.9902 3.7618/0.9836 2.1329/0.9917 3.4159/0.9863 1.4296/0.9955 3.1048/0.9914
    LapSRN[18] 2.3995/0.9913 4.2889/0.9834 1.9860/0.9920 3.5537/0.9852 1.4702/0.9957 3.6258/0.9878
    PDRN 2.1698/0.9923 3.6122/0.9878 1.8003/0.9927 3.0407/0.9884 1.3034/0.9962 2.6953/0.9926
    下载: 导出CSV

    表 8  训练时间(小时)

    Table 8.  Training time(hour)

    方法 16×
    SRCNN[13] 12.35 12.65 13.16
    VDSR[15] 4.65 4.59 4.86
    LapSRN[18] 4.93 5.08 5.13
    本文(标准初始化) 5.97 6.53 6.80
    本文(预训练初始化) 1.49 1.63 1.70
    下载: 导出CSV
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出版历程
收稿日期:  2018-11-14
修回日期:  2019-03-12
刊出日期:  2019-11-01

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