Visual similarity simulation of slub denim based on image processing

Abstract

The slub denim has various appearance styles that attract consumers due to the irregular distribution and long cycle of the slubs in the slub yarn. This characteristic of the slub yarn makes the slub denim proofing that relies on artificial visual contrast time-consuming with a long design cycle. A visual similarity simulation method was proposed for slub denim based on slub yarn images to solve this problem. At first, the yarn images were pre-processed by grey-level transformation, image threshold segmentation, and morphological operations to obtain the yarn core images. Secondly, according to the elastic change of yarn in fabric structure, the elliptic and sine curve models were established to treat the string to obtain the actual yarn shape in the fabric. Then, a light intensity curve function, which consists of a radial curve model and an interlacing point curve model, was established to simulate the light intensity distribution on the yarn surface. Finally, the cover relation of warp and weft yarns was controlled by Boolean matrix, and mean filtering was used to adjust the fuzzy denim fabric images. The experimental results show that the slub denims simulated by the method proposed in this article have a high visual similarity with the actual slub denims. Meanwhile, multiple parameters of the simulation model established in this article can be adjusted to enhance the adaptability of the model and the accuracy of the simulation results.