In submarine and underwater detection tasks, conventional optical imaging and analysis methods are not universally applicable due to the limited penetration depth of visible light. Instead, sonar imaging has become a preferred alternative. However, the capture and transmission conditions in complicated and dynamic underwater environments inevitably lead to visual quality degradation of sonar images, which might also impede further recognition, analysis and understanding. To measure this quality decrease and provide a solid quality indicator for sonar image enhancement, we propose a task- and perception-oriented sonar image quality assessment (TPSIQA) method, in which a semi-reference (SR) approach is applied to adapt to the limited bandwidth of underwater communication channels. In particular, we exploit reduced visual features that are critical for both human perception of and object recognition in sonar images. The final quality indicator is obtained through ensemble learning, which aggregates an optimal subset of multiple base learners to achieve both high accuracy and a high generalization ability. In this way, we are able to develop a compact but generalized quality metric using a small database of sonar images. Experimental results demonstrate competitive performance, high efficiency, and strong robustness of our method compared to the latest available image quality metrics.

中文翻译：

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基于任务和视觉感知的半参考声纳图像质量评估


在潜艇和水下探测任务中，由于可见光穿透深度有限，传统的光学成像和分析方法并不普遍适用。相反，声纳成像已成为首选替代方案。然而，复杂动态的水下环境中的捕获和传输条件不可避免地导致声纳图像的视觉质量下降，这也可能阻碍进一步的识别、分析和理解。为了测量这种质量下降并为声纳图像增强提供可靠的质量指标，我们提出了一种面向任务和感知的声纳图像质量评估（TPSIQA）方法，其中应用半参考（SR）方法来适应水下通信信道的带宽有限。特别是，我们利用了减少的视觉特征，这些特征对于人类对声纳图像的感知和物体识别至关重要。最终的质量指标是通过集成学习获得的，集成学习聚合了多个基学习器的最优子集，以实现高精度和高泛化能力。通过这种方式，我们能够使用小型声纳图像数据库开发紧凑但通用的质量度量。实验结果表明，与最新的可用图像质量指标相比，我们的方法具有竞争性能、高效率和强大的鲁棒性。