Underwater Imaging System Based on Light Field Technology

Ouyang, Feng; Yu, Jia; Liu, Huiping; Ma, Zhen; Xiao, Yu

doi:10.1109/jsen.2021.3069246

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Skinner [18] applied 2D defogging methods to each sub-aperture image and processed them using guided image filtering, resulting in 4D defogged underwater light field images. Feng [19] established an underwater imaging system based on Mie scattering theory for underwater 3D reconstruction and image restoration. Additionally, Lu [20] introduced depth convolutional neural fields in image restoration tasks to address the desaturation problem in light field images.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Underwater Image Restoration and Depth Estimation with Light Field Images

Xiao,

Gao,

Huang

2024

JMSE

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Methods based on light field information have shown promising results in depth estimation and underwater image restoration. However, improvements are still needed in terms of depth estimation accuracy and image restoration quality. Previous work on underwater image restoration employed an image formation model (IFM) that overlooked the effects of light attenuation and scattering coefficients in underwater environments, leading to unavoidable color deviation and distortion in the restored images. Additionally, the high blurriness and associated distortions in underwater images make depth information extraction and estimation very challenging. In this paper, we refine the light propagation model and propose a method to estimate the attenuation and backscattering coefficients of the underwater IFM. We simplify these coefficients into distance-related functions and design a relationship between distance and the darkest channel to estimate the water coefficients, effectively suppressing color deviation and distortion in the restoration results. Furthermore, to increase the accuracy of depth estimation, we propose using blur cues to construct a cost for refocusing in the depth direction, reducing the impact of high signal-to-noise ratio environments on depth information extraction, and effectively enhancing the accuracy and robustness of depth estimation. Finally, experimental comparisons show that our method achieves more accurate depth estimation and image restoration closer to real scenes compared to state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%