Optics and Precision Engineering, Volume. 30, Issue 15, 1868(2022)

Visible light video denoising and FPGA hardware implementation

Sixian ZHAO1,2, Minjie WAN1,2、*, Weixian QIAN1,2, Lin ZHOU1,2, Ajun SHAO1,2, Qian CHEN1,2, and Guohua GU1,2、*
Author Affiliations
  • 1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing20094, China
  • 2Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing10094, China
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    Figures & Tables(18)
    Flow chart of spatio-temporal domain video denoising algorithm
    DOG filter result of one frame
    Flow chart of domain division filtering in time domain
    Filtering results of DOG
    Hardware architecture of denoising algorithm in this paper
    Cameralink decodes data
    IP core of HLS module
    Schematic diagram of convolution filter window
    IP core of FDMA
    Packet data flow
    Hardware platform of denoising algorithm in this paper
    Denoising results of software and hardware of proposed algorithm and denoising results of different algorithms
    Resource occupancy
    • Table 1. Comparison results of scene 1-3

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      Table 1. Comparison results of scene 1-3

      算 法场景1场景2场景3
      PSNRSSIMt/sPSNRSSIMt/sPSNRSSIMt/s
      NSCT33.592 80.921 750.208 820.204 20.801 249.547 628.458 20.655 349.615 6
      FOE34.598 10.869 12.591 527.449 30.535 72.690 428.414 00.532 22.699 3
      金字塔33.548 70.834 70.325 421.534 80.514 30.402 428.376 90.616 60.396 5
      BM3D33.022 70.822 00.399 420.204 20.487 20.409 427.827 80.602 40.425 5
      MATLAB实现34.878 30.940 60.392 327.956 30.581 60.390 628.845 30.694 00.357 8
      FPGA实现34.610 80.921 00.159 326.734 80.573 20.153 428.276 40.657 60.126 5
    • Table 2. Comparison results of scene 4-6

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      Table 2. Comparison results of scene 4-6

      算 法场景4场景5场景6
      PSNRSSIMt/sPSNRSSIMt/sPSNRSSIMt/s
      NSCT24.772 20.699 649.323 515.452 00.664 149.378 825.343 10.662 249.409 0
      FOE23.586 90.582 43.062 029.345 50.673 12.882 225.523 40.480 72.674 7
      金字塔23.312 30.585 60.366 129.421 60.731 20.391 424.746 50.653 40.342 1
      BM3D23.311 00.536 80.366 530.257 30.715 60.394 221.266 30.441 40.3 594
      MATLAB实现23.636 20.790 10.366 030.329 60.797 20.384 725.585 60.681 70.320 8
      FPGA实现23.354 20.784 50.131 229.965 70.742 30.135 425.514 30.669 80.131 1
    • Table 3. Comparison results of scene 7-9

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      Table 3. Comparison results of scene 7-9

      算 法场景7场景8场景9
      PSNRSSIMt/sPSNRSSIMt/sPSNRSSIMt/s
      NSCT14.625 70.580 749.329 124.371 80.494 948.627 620.233 40.643 847.169 1
      FOE19.639 50.505 73.060 227.140 90.445 82.659 620.630 40.667 42.985 6
      金字塔19.027 60.565 50.369 927.106 40.493 40.391 620.385 50.650 20.341 1
      BM3D19.011 10.485 60.377 926.386 60.493 50.407 420.588 50.636 90.345 0
      MATLAB实现19.643 80.660 90.352 927.612 80.498 00.350 820.689 80.690 30.329 0
      FPGA实现19.519 70.646 70.134 427.287 40.489 90.139 820.601 20.982 60.128 7
    • Table 4. Comparison results of scene 10-12

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      Table 4. Comparison results of scene 10-12

      算 法场景10场景11场景12
      PSNRSSIMt/sPSNRSSIMt/sPSNRSSIMt/s
      NSCT6.548 00.303 647.888 620.863 30.743 946.866 820.806 60.618 250.100 1
      FOE26.215 40.352 92.578 222.858 20.750 52.941 926.528 20.530 72.343 6
      金字塔23.453 40.296 30.401 621.933 40.769 20.401 726.470 30.593 70.406 8
      BM3D20.694 30.270 50.414 421.596 50.727 80.414 426.295 30.554 20.421 3
      MATLAB实现26.435 60.353 30.361 022.127 00.779 60.359 626.528 80.661 60.361 9
      FPGA实现26.388 90.340 80.141 922.012 10.771 30.137 526.493 30.650 00.140 8
    • Table 5. Comparison results of scene 13-15

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      Table 5. Comparison results of scene 13-15

      算 法场景13场景14场景15
      PSNRSSIMt/sPSNRSSIMt/sPSNRSSIMt/s
      NSCT17.650 10.696 247.123 121.099 40.724 046.866 821.929 70.697 449.619 9
      FOE21.465 80.417 02.987 129.164 30.821 22.961 923.515 50.600 22.688 2
      金字塔21.748 60.417 60.416 527.748 70.727 60.389 723.211 10.647 80.402 7
      BM3D21.928 80.325 50.438 326.118 60.760 60.414 422.471 40.367 40.415 3
      MATLAB实现21.937 60.453 20.394 729.438 80.826 80.343 123.827 80.706 60.361 2
      FPGA实现21.905 60.421 60.112 329.170 80.821 80.189 323.387 00.696 70.1 369
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    Sixian ZHAO, Minjie WAN, Weixian QIAN, Lin ZHOU, Ajun SHAO, Qian CHEN, Guohua GU. Visible light video denoising and FPGA hardware implementation[J]. Optics and Precision Engineering, 2022, 30(15): 1868

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    Paper Information

    Category: Information Sciences

    Received: Mar. 25, 2022

    Accepted: --

    Published Online: Sep. 7, 2022

    The Author Email: Minjie WAN (minjiewan1992@njust.edu.cn), Guohua GU (gghnjust@mail.njust.edu.cn)

    DOI:10.37188/OPE.20223015.1868

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