面向流式水下成像声纳的自监督压缩与伪影校正
Self-Supervised Compression and Artifact Correction for Streaming Underwater Imaging Sonar
AI总结：
SCOPE通过自监督方法实现低比特率水下声纳流式传输，有效压缩并校正声纳伪影，提升实时检测性能。
摘要：
实时成像声纳在水下监测中至关重要，特别是在光学传感失效的情况下，但其应用受到低上行带宽和特定声纳伪影（斑点、运动模糊、回波、声学阴影）的限制，这些伪影影响多达98%的帧。我们提出了SCOPE，一个自监督框架，能够在没有干净-噪声对或合成假设的情况下联合执行压缩和伪影校正。SCOPE结合（i）自适应码本压缩（ACC），它学习针对声纳定制的频率编码潜在表示，与（ii）频率感知多尺度分割（FAMS），它将帧分解为低频结构和稀疏高频动态，同时抑制快速波动的伪影。进一步的 hedging 训练策略通过生成无标签的低通代理对来引导频率感知学习。在数月的现场ARIS声纳数据上评估，SCOPE实现了结构相似性指数（SSIM）为0.77，比先前的自监督去噪基线提高了40%，在比特率降至≤0.0118 bpp时。它将上行带宽减少了超过80%，同时提高了下游检测性能。系统以实时运行，嵌入式GPU上编码时间为3.1毫秒，服务器端完整多层解码时间为97毫秒。SCOPE已部署在三个太平洋西北河流中，以支持野外实时大马哈鱼计数和环境监测。结果表明，学习频率结构化的潜在表示能够在实际部署条件下实现低比特率声纳流传输，同时保留信号细节。
英文摘要：
Real-time imaging sonar is crucial for underwater monitoring where optical sensing fails, but its use is limited by low uplink bandwidth and severe sonar-specific artifacts (speckle, motion blur, reverberation, acoustic shadows) affecting 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
