A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement.

This project looks at whether super‑resolution (SR) image reconstruction can let vision‑based sensors measure farther away while still capturing dynamic responses accurately. Vision sensors are popular for structural health monitoring and vibration analysis because they don’t need to touch the structure. Their spatial resolution, however, drops a lot at long distances, which hurts measurement accuracy. To solve this, the work shows how to add SR algorithms—either deep‑learning models or interpolation methods—to turn low‑resolution, long‑range video into high‑resolution frames. Those frames are then analyzed with sub‑pixel displacement tracking tools such as digital image correlation (DIC) or optical flow. This extracts quantities like displacement and vibration frequency. The study compares computational load,

reconstruction quality, and measurement accuracy for different distances. By providing step‑by‑step development guidance, it demonstrates that SR pre‑processing can offset the limits of cheap optics, allowing affordable, long‑range dynamic monitoring without needing expensive high‑resolution cameras.

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