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结合深度图和红通道最小强度先验的水下图像复原

Underwater Image Restoration Via Scene Depth and Minimum Intensity Prior of Red Channel

  • 摘要: 针对水下图像雾化、模糊等问题, 提出结合深度图和红通道最小强度先验的水下图像复原变分方法. 基于完整的水下成像模型, 首先设计了一种自适应加权融合亮度、梯度及色差等信息的场景深度估计方法, 计算3个通道的透射率; 然后根据前向散射分量建立变分模型的数据项, 对拟恢复图像引入红通道最小强度先验作为变分能量方程规则项, 借助于图像金字塔, 采用粗尺度到细尺度逐步优化策略进行模糊核估计; 最后利用交替方向乘子法迭代求解, 解决变分模型带来的非光滑优化问题. 在UIEB数据集上进行了定性和定量实验, 通过UCIQE, FADE和CPBD客观评价指标对比, 结果表明, 所提方法的评价结果比经典方法平均分别提升15%以上, 复原后的图像具有更高的清晰度和更丰富的边缘信息.

     

    Abstract: Underwater images often suffer from hazy and blur due to absorption and scattering effects. To address these problems, we propose a variational framework combining scene depth map and minimum intensity prior of red channel. Depending on the complete underwater image formation model, an adaptive weighted strategy is first designed to estimate the scene depth by fusing the information of brightness, gradient and chromatic aberration. After that, the transmittance of three color channels can be further precisely calculated. Then, in the proposed variational model, the data fidelity term is established according to the forward scattering, and the minimum intensity prior of red channel is regarded as the regular term. Using image pyramid, blur kernel estimation is further carried out by one stepwise optimization strategy from coarse to fine scales. Finally, the alternating direction multiplier method is employed to speed up the non-smooth optimization of the proposed variational model. Qualitative and quantitative experiment is conducted on the UIEB dataset, comparing with several state-of-the-art methods, the proposed method obtains increase by 15% across three quality evaluation metrics in terms of UCIQE, FADE and CPBD, and the restored images have greater clarity and richer edge information.

     

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