Tie-Dyeing Pattern Fast-Generation Method Based on Deep-Learning and Digital-Image-Processing Technology

Abstract

Abstract Contingency and uniqueness are regarded as typical artistic characteristics. To accomplish the realistic effect of each tie-dyeing pattern artwork, we propose a digital tie-dyeing pattern fast-generation algorithm based on auxiliary-classifier deep-convolution generative adversarial network (AC-DCGAN) and image-processing technology. To apply this algorithm, the designer first draws the planar layout diagram of the tie-dyeing patterns. The diagram consists of a white background and polychrome circles, and the regional-connectivity algorithm is used to extract information on all the circle positions as well as the pattern categories in the diagram. Then the AC-DCGAN-generated background image is color-corrected to stitch and complete its construction. The AC-DCGAN-generated tie-dyeing pattern image is also color-corrected and is then segmented and copied to the circle area. Mean filtering creates the final digital tie-dyeing patterns. Results show no obvious color difference in generated patterns, splicing edges show uniform transition, and unique patterns exhibit tie-dye characteristics, achieving realistic artistic effects.