范逸辉, 金欣, 邓儒嘉, 谢佳宇, 孙科林, 杨景川, 张兵. 用于深海视频去散射的深度校正散射统计模型[J]. 红外与激光工程, 2022, 51(9): 20210919. DOI: 10.3788/IRLA20210919
引用本文: 范逸辉, 金欣, 邓儒嘉, 谢佳宇, 孙科林, 杨景川, 张兵. 用于深海视频去散射的深度校正散射统计模型[J]. 红外与激光工程, 2022, 51(9): 20210919. DOI: 10.3788/IRLA20210919
Fan Yihui, Jin Xin, Deng Rujia, Xie Jiayu, Sun Kelin, Yang Jingchuan, Zhang Bing. Depth-rectified statistical scattering modeling for deep-sea video descattering[J]. Infrared and Laser Engineering, 2022, 51(9): 20210919. DOI: 10.3788/IRLA20210919
Citation: Fan Yihui, Jin Xin, Deng Rujia, Xie Jiayu, Sun Kelin, Yang Jingchuan, Zhang Bing. Depth-rectified statistical scattering modeling for deep-sea video descattering[J]. Infrared and Laser Engineering, 2022, 51(9): 20210919. DOI: 10.3788/IRLA20210919

用于深海视频去散射的深度校正散射统计模型

Depth-rectified statistical scattering modeling for deep-sea video descattering

  • 摘要: 深海探测目前广泛应用于环境、结构监测和油气勘探等领域,越来越受到各国的重视。而散射现象严重降低了深海探测中的视觉图像质量,且现有的方法在多深度或非均匀照明的深海散射环境中均受限。因此,文中提出了一种基于深度校正统计散射模型的深海去散射方法,提出的模型利用透射图建模了深度归一化的散射图像,并利用高斯统计模型估计局部散射,得到每个颜色通道中深度校正的散射图,从而实现在多深度和非均匀照明情况下对散射的精确建模。为了验证笔者算法的有效性和鲁棒性,在浅海和深海不同场景的图像上进行了测试,同时也在深海的视频序列上进行了测试,实验结果均表明,提出的方法在主观质量和客观评价方面均优于现有方法。

     

    Abstract: Deep-sea exploration is widely used in fields of environment and structural monitoring as well as exploration for oil and gas, which has attracted more attention in many countries of the world. In deep-sea exploration, the scattering phenomenon seriously reduces the visual image quality. Existing methods are limited in deep-sea scattering environments with multi-depth or non-uniform illumination. Thus, a deep-sea descattering method based on a depth-rectified statistical scattering model is proposed. The model proposed uses the transmission map to model the depth-constant scattered image, and uses the Gaussian statistical model to estimate the local scattering to obtain the depth-rectified scattering map in each color channel, so as to achieve the accurate modeling of scattering at multi-depth and non-uniform illumination scenarios. In order to demonstrate the effectiveness and robustness of proposed algorithm, we conducted tests on images of different scenes in shallow sea and deep sea, as well as on video sequences in deep-sea. Experimental results show that the proposed method outperforms existing methods in subjective quality and objective evaluation.

     

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