Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. The code is publicly available at: https://github.com/zhoujingchun03/SMDR-IS.

翻译：视觉上恢复水下场景主要涉及减轻水下介质的干扰。现有方法忽略了水下场景中固有的尺度相关特性。为此，我们提出了基于内在监督的协同多尺度细节精炼方法（SMDR-IS），用于增强水下场景细节。该方法包含多个阶段。来自原始图像的低退化阶段通过自适应选择性内在监督特征（ASISF）模块的特征传播，为原始阶段提供多尺度细节。通过内在监督，ASISF模块能够精确控制和引导多退化阶段之间的特征传递，增强多尺度细节精炼，并最大限度地减少低退化阶段中无关信息的干扰。在SMDR-IS的多退化编码器-解码器框架中，我们引入了双焦点内在上下文注意力模块（BICA）。基于内在监督原理，BICA高效利用图像中的多尺度场景信息。BICA通过利用低分辨率空间的信息来引导高分辨率空间，强调了空间上下文关系在水下图像恢复中的关键作用。在训练过程中，引入多退化损失函数可以增强网络性能，使其能够灵活提取不同尺度的信息。与最先进的方法相比，SMDR-IS持续展现出优越的性能。代码已公开，地址为：https://github.com/zhoujingchun03/SMDR-IS。