Advanced Imaging, Volume. 2, Issue 1, 012001(2025)

Underwater optical imaging and sensing in turbidity using three-dimensional integral imaging: a review

Alex Maric, Gokul Krishnan, Rakesh Joshi, Yinuo Huang, Kashif Usmani, and Bahram Javidi*
Author Affiliations
  • Department of Electrical and Computer Engineering, University of Connecticut, Storrs, USA
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    Figures & Tables(10)
    3D integral imaging. (a) Pickup stage[12]. (b) Reconstruction stage[12]. (c) 3D underwater computational reconstruction[18]. Reprinted with permissions from Refs. [12] and [18].
    (a) Clear water 3D scene used in the experiments. (b) The scene in turbid water. (c) Diagram of the 3D underwater imaging system in turbid water. (d)–(g) 3D integral imaging reconstruction. (d) 3D reconstructed bug focused at zr=490 mm, (e) 3D reconstructed treasure focused at zr=640 mm, (f) 3D reconstructed small fish focused at zr=730 mm, and (g) 3D reconstructed large fish focused at zr=770 mm. Reprinted with permission from Ref. [18].
    Flow chart of peplography for imaging in scattering media. Reprinted with permission from Ref. [19].
    (a) Sample clean image of a light source (α=0.0025 mm−1). (b)–(c) Degraded images in high turbid water (α=0.040 mm−1). (d) Recovered clean image from (b). (e) Recovered clean image from (c). α is Beer’s coefficient. Reprinted with permission from Ref. [21].
    (a) Sample clean image of a light source (α=0.0025 mm−1). (b) 2D degraded central perspective image in turbidity, ambient light, and partial occlusion (α=0.008 mm−1). (c) Recovered clean 2D image from (b). (d) Recovered clean 3D reconstructed image from (b). α is Beer’s coefficient. Reprinted with permission from Ref. [21].
    Flow chart of the underwater optical signal detection pipeline using 4D nonlinear correlation. Reprinted with permission from Ref. [12].
    Flow chart of CNN-BiLSTM-based signal detection using sliding window-based classification. Reprinted with permission from Ref. [13].
    Flow chart of end-to-end integrated 1DInImCNN-based signal detection model. Reprinted with permissions from Refs. [14,15].
    (a)–(c) Sample training data without occlusion. (a) Central perspective video of the encoded optical signal in clear water. (b)–(c) Sample training data in turbid water from a lensless-based 1D camera array with diffusers at α=0.0037 mm−1 and α=0.0095 mm−1, respectively. (d)–(f) Sample testing data with occlusion. (d) Central perspective video of the encoded optical signal in clear water with partial occlusion. (e)–(f) Sample testing data in turbid water with partial occlusion from a lensless-based 1D camera array with diffusers at α=0.0170 mm−1 and α=0.0198 mm−1, respectively. Reprinted with permission from Ref. [15].
    Sample underwater testing scenes with occlusion applying object detection and signal detection. Reprinted with permission from Ref. [16].
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    Alex Maric, Gokul Krishnan, Rakesh Joshi, Yinuo Huang, Kashif Usmani, Bahram Javidi, "Underwater optical imaging and sensing in turbidity using three-dimensional integral imaging: a review," Adv. Imaging 2, 012001 (2025)

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    Paper Information

    Category: Review Article

    Received: May. 8, 2024

    Accepted: Oct. 17, 2024

    Published Online: Nov. 21, 2024

    The Author Email: Bahram Javidi (bahram.javidi@uconn.edu)

    DOI:10.3788/AI.2025.20002

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