保形化速率自适应感知（Conformalized Rate-Adaptive Sensing）
Conformalized Rate-Adaptive Sensing
浏览论文内容
中文总结 AI 辅助
针对高分辨率成像系统何时收集足够测量数据以准确重建图像的问题，提出CoRAS方法自适应选采集速率，实验显示其覆盖率达标且平均用更少测量、难重建图像获更多测量。
中文摘要 AI 辅助
许多高分辨率成像系统都面临一个核心问题：何时收集到足够的测量数据以准确重建图像？我们提出Conformalized Rate-Adaptive Sensing（CoRAS）方法，该方法可针对每张图像自适应选择采集或压缩速率，同时以高概率保证重建误差低于目标水平。随着测量数据的收集，图像重建模型会逐步恢复真实图像，生成关于采集速率的重建路径；CoRAS利用该路径至早期决策时刻，估计目标停止时间——即重建误差首次降至目标水平以下的时刻，再通过具有相似早期重建行为的图像校准该估计，生成停止时间的上界，具备边际和近似条件覆盖率保证。在图像数据集上的实验表明，CoRAS达到了目标停止时间覆盖率，平均使用的测量数据少于固定速率停止规则，且为更难重建的图像分配更多测量数据。
英文摘要
Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method that adaptively chooses an acquisition or compression rate for each image while keeping the reconstruction error below a target level with high probability. As measurements are collected, an image reconstruction model gradually recovers the true image, producing a reconstruction path over acquisition rates. CoRAS uses this path up to an early decision time to estimate the target stopping time, defined as the first time at which the reconstruction error falls below the target level. It then calibrates this estimate using images with similar early reconstruction behavior, producing an upper bound on the stopping time with marginal and approximate conditional coverage guarantees. Experiments on image datasets show that CoRAS attains the target stopping-time coverage, uses fewer measurements on average than fixed-rate stopping rules, and assigns more measurements to images that are harder to reconstruct.
发表机构
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
