Real-time imaging sonar has become an important tool for underwater monitoring in environments where optical sensing is unreliable. Its broader use is constrained by two coupled challenges: highly limited uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) that affect up to 98% of frames. We present SCOPE, a self-supervised framework that jointly performs compression and artifact correction without clean-noise pairs or synthetic assumptions. SCOPE combines (i) Adaptive Codebook Compression (ACC), which learns frequency-encoded latent representations tailored to sonar, with (ii) Frequency-Aware Multiscale Segmentation (FAMS), which decomposes frames into low-frequency structure and sparse high-frequency dynamics while suppressing rapidly fluctuating artifacts. A hedging training strategy further guides frequency-aware learning using low-pass proxy pairs generated without labels. Evaluated on months of in-situ ARIS sonar data, SCOPE achieves a structural similarity index (SSIM) of 0.77, representing a 40% improvement over prior self-supervised denoising baselines, at bitrates down to <= 0.0118 bpp. It reduces uplink bandwidth by more than 80% while improving downstream detection. The system runs in real time, with 3.1 ms encoding on an embedded GPU and 97 ms full multi-layer decoding on the server end. SCOPE has been deployed for months in three Pacific Northwest rivers to support real-time salmon enumeration and environmental monitoring in the wild. Results demonstrate that learning frequency-structured latents enables practical, low-bitrate sonar streaming with preserved signal details under real-world deployment conditions.

翻译：实时成像声呐已成为光学传感不可靠环境下水下监测的重要工具。其更广泛应用受到两个相互关联的挑战制约：高度有限的上行链路带宽以及严重影响高达98%帧数的声呐特有伪影（散斑、运动模糊、混响、声学阴影）。本文提出SCOPE，一种无需干净-噪声配对或合成假设即可联合执行压缩与伪影校正的自监督框架。SCOPE融合了（i）自适应码本压缩（ACC）——学习针对声呐数据定制的频率编码潜在表征，与（ii）频率感知多尺度分割（FAMS）——将帧分解为低频结构与稀疏高频动态分量，同时抑制快速波动的伪影。套期训练策略进一步利用无标签生成的低通代理对指导频率感知学习。基于数月原位ARIS声呐数据的评估表明，SCOPE在比特率低至≤0.0118 bpp时达到0.77的结构相似性指数（SSIM），较现有自监督去噪基线提升40%。该系统在降低上行链路带宽超过80%的同时提升了下游检测性能，可在嵌入式GPU上以3.1毫秒完成实时编码，并在服务器端实现97毫秒的全多层解码。SCOPE已在太平洋西北流域的三条河流中部署数月，支持野外实时鲑鱼计数与环境监测。结果表明，学习频率结构化潜在表征能够在实际部署条件下实现保留信号细节的实用化低比特率声呐流式传输。