Conformalized Rate-Adaptive Sensing

2026-07-29

AI Summary

This work addresses a key challenge in high-resolution imaging: adaptively determining when to terminate sampling while ensuring reconstruction accuracy. The authors propose CoRAS, the first method to integrate conformal prediction into adaptive sensing, dynamically adjusting the sampling rate along the image reconstruction trajectory. By leveraging conformal calibration based on similar reconstruction behaviors, CoRAS provides an upper bound on stopping time with marginal and approximate conditional coverage guarantees. Combining a reconstruction model with an early-decision mechanism, the approach achieves target coverage with fewer average measurements across multiple datasets and automatically allocates additional sampling resources to images that are more difficult to reconstruct.

Abstract

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.

Problem

• adaptive sensing
• image reconstruction
• stopping time
• measurement efficiency
• error control

Innovation

• conformal prediction
• adaptive sensing
• image reconstruction
• stopping time
• rate-adaptive compression