Development and validation of a digital image-based method for color assessment of PET fabrics dyed in supercritical CO2 and aqueous media

Digital image-based color evaluation has emerged as a promising approach for non-contact, large-area color assessment in textile manufacturing. However, its applicability across fundamentally different dyeing systems remains insufficiently investigated. This study presents a calibrated stereo-photometric digital image-based framework for comprehensive color characterization of polyethylene terephthalate (PET) fabrics dyed using conventional aqueous and supercritical carbon dioxide (scCO₂) systems. Digital images acquired under controlled illumination were processed through image fusion, color calibration, and region-of-interest analysis to quantify color depth, spatial uniformity, and perceptual color attributes in the CIE L C h color space. Image-derived measurements showed high correlations with conventional spectrophotometric measurements for the CIE tristimulus Y value and Kubelka–Munk color strength ( K / S ). Linear relationships between image-derived and spectrophotometric CIE L a b* values demonstrated consistent relative color trends between the two methods across both dyeing systems, although differences in absolute colorimetric values remained. Beyond comparison with conventional spectrophotometry, the framework enabled simultaneous full-field color characterization, providing richer color information than conventional point-based measurements. These results highlight the potential of the framework as a complementary approach for quantitative textile color evaluation and provide a foundation for future computer vision- and AI-assisted textile quality inspection.