Image‐Based Estimation of Porosity and Tortuosity in Fibrous Acoustic Absorbers

ABSTRACT This study presents a fast and non‐destructive image‐based method for estimating two key acoustic parameters—open porosity and tortuosity—in fibrous sound‐absorbing materials. The approach uses a single grayscale optical micrograph, which is down‐sampled, contrast‐equalized, and segmented via adaptive thresholding. From the resulting binary fiber mask, two geometric descriptors are extracted: coverage and a one‐pixel‐wide skeleton. Porosity is estimated using a simple linear formula calibrated on three reference materials, yielding an average absolute error below 0.3% when compared with argon gas pycnometry. Tortuosity is inferred from the total skeleton length relative to the image area, producing a stable ranking across materials with consistent bias relative to measured data. Additionally, a random forest model using only three image features—coverage, median fiber radius, and skeleton length—predicts airflow resistivity with over 70% explained variance. The full analysis pipeline is implemented in Python using open‐source libraries (OpenCV, scikit‐image) and runs in under half a second per image on standard hardware. This makes the method well suited for early‐stage material screening, in‐line quality control, or laboratory support, without the need for destructive testing or costly instruments. The approach bridges the gap between optical imaging and physical parameter estimation, offering a lightweight alternative to traditional porosity and impedance‐tube measurements.