Underwater image restoration based on improved dark channel prior

Wang, Yingbo; Cao, Jie; Tang, Mingyuan; Li, Guoliang; Hao, Qun; Fang, Yami

doi:10.1117/12.2586656

Cited by 1 publication

(1 citation statement)

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“…However, due to the influence of the artificial light source, the center brightness of the underwater crab image is larger, which leads to an excessive higher atmospheric light value of the solution and affects the real image solution. The quadtree algorithm [25,26] is employed to optimize the atmospheric light solution candidate region of the underwater crab image to reduce the data of the atmospheric light value and ensure the effectiveness of the image processing. The algorithm sets the quartiles of standard deviation as the evaluation function and selects the suitable quartile threshold after the experiment.…”

Section: Improved Dark Channel Prior Approachmentioning

confidence: 99%

Rethinking Underwater Crab Detection via Defogging and Channel Compensation

Sun,

Yuan,

et al. 2024

Fishes

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Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming.

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Section: Improved Dark Channel Prior Approachmentioning

confidence: 99%