Underwater imaging often suffers from poor quality due to the complex underwater environment and limitations of hardware equipment, leading to images with shallow depth of field and moving objects, which pose a challenge for information fusion of image sequences from the same underwater scene. To effectively address these problems, we propose a decoupled variational Retinex method for reconstructing and fusing underwater shallow depth of field images. Specifically, we first construct a module that adopts the decoupled variational Retinex model to adjust pixel dynamic range and luminance components, enhance non-local properties’ extraction with higher-order data constraints, and significantly improve image quality. Then, we develop a precision alignment strategy for image sequences by calculating and correcting control point deviations in the overlapping areas, achieving accurate registration of the image sequences, and effectively reconstructing scenes with parallax. Moreover, scenes with moving objects within the image sequence are reconstructed by redistributing overlapping areas. We design a novel cost function based on the neighborhood information of seams, which facilitates iterative optimization of these solved seams. This process improves the segmentation accuracy within these regions, achieving more precise scene reconstruction. Compared with state-of-the-art approaches, our method demonstrates superior performance in rectifying degraded image quality and reconstructing visually appealing images, with the resulting reconstructed images showing enhanced subjective visual quality.

中文翻译：

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用于视差和运动物体的水下浅景深图像重建和融合的解耦变分视网膜


由于复杂的水下环境和硬件设备的限制，水下成像往往质量较差，导致图像景深较浅且物体运动，这给同一水下场景的图像序列的信息融合带来了挑战。为了有效解决这些问题，我们提出了一种解耦变分 Retinex 方法来重建和融合水下浅景深图像。具体来说，我们首先构建一个模块，采用解耦变分Retinex模型来调整像素动态范围和亮度分量，通过高阶数据约束增强非局部属性的提取，并显着提高图像质量。然后，我们通过计算和校正重叠区域中的控制点偏差，制定图像序列的精确对齐策略，实现图像序列的精确配准，并有效地重建视差场景。此外，图像序列中具有移动物体的场景是通过重新分布重叠区域来重建的。我们根据接缝的邻域信息设计了一种新颖的成本函数，这有助于对这些已解决的接缝进行迭代优化。该过程提高了这些区域内的分割精度，实现更精确的场景重建。与最先进的方法相比，我们的方法在纠正退化的图像质量和重建视觉上吸引人的图像方面表现出卓越的性能，所得重建图像显示出增强的主观视觉质量。