Improving color reliability of digital textile images via optimized acquisition and preprocessing

As textile industries move toward greater digitalization and automation, accurate and reproducible image-based analysis of textile surfaces has become increasingly important across research, manufacturing, and digital commerce. In this study, we propose an integrated imaging and preprocessing framework that significantly improves the color reliability of digital textile images, thereby enhancing the precision of textile image analysis across a wide range of applications. We compared the extent of color distortion in digital images of samples from 11 different textile under different background and surrounding colors (white or black). We evaluated the effectiveness of image fusion as a preprocessing step by comparing the color reliability and image quality of non-fused images. We then derived optimal settings for image acquisition environments and preprocessing strategies for digital images of textile surfaces. To validate the proposed guidelines, we compared the color distortion in digital images corrected using only conventional color correction, and to digital images obtained using our proposed guidelines before color correction. Our findings demonstrated that applying appropriate physical environments and image preprocessing significantly increases the color stability and reliability of digitally represented textile colors, yielding an average improvement of 21.2% in their visual fidelity to the original fabric.