# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 1 Introduction  
Digital inkjet printing technology has evolved along with the continuous integration and development of electronic computer technology and high-precision instrument technology. This technology is the key solution for the textile industry to address the demand for personalized customization and small-batch textile production. Digital inkjet printing technology involves inputting patterns and designs as digital signals into a computer, followed by editing and processing through a computer-controlled color separation drafting system. Subsequently, the computer-controlled printer nozzle directly sprays specialized ink onto the textile and mixes it to form the desired pattern. However, this technology has challenges in accurately reproducing the color on the fabric to achieve the desired color effect in the actual print [1], due to the limitation of the printer's available color gamut [2], color-mixing algorithm, and surface texture of textiles [3]. Traditional color prediction methods are often based on manual experience and proofing adjustments, which are subjective, inconsistent and labor-intensive. The development of an accurate and reliable color prediction method is therefore essential to the advancement of digital printing technology in textile. Color prediction is key to color reproduction and its main purpose is to predict the expected value of color reproduction results in a digital environment. The process of color prediction is based on color chromaticity information and the prediction of colorreproduction results under specific light source conditions to achieve accurate control of color output [4]. It is known that a textile sample's surface texture influences its visual color [5]; thus, it is also important that color prediction methods account for different textiles. Current solutions for color prediction in digital printing include ICC (International Color Consortium) [6] profiles, prediction methods implemented with machine learning algorithms, and print tests. In the field of deep learning, image-to-image conversion methods [7] based on generative adversarial networks (GAN), which learn mapping functions between source and target domains based on training images, have been successfully applied to tasks such as image coloring [8], style conversion [9], image restoration and image segmentation [10]. Pix2 Pix is a conditional GAN-based image translation framework that enables mapping from input images to output images. Silk, being an expensive fabric, possesses unique qualities and aesthetics. However, there are still challenges in color control for silk, requiring multiple proofing adjustments to achieve accurate colors in actual industrial settings. Silk fiber also differs from other fibers with its distinctive microscopic structure, resulting in unique coloration effects. Silk products are typically positioned in the high-end market, which demands stringent color requirements, necessitating the attainment of the highest level of color accuracy and consistency. Therefore, selecting silk as the research subject holds practical significance. Furthermore, silk finds extensive applications in the textile industry, including fashion, interiors, art, and gifts, among other areas. Thus, precise control and prediction of silk colors hold significant importance, enabling designers, manufacturers, and consumers to better fulfill their requirements and creative visions. By studying color differences of silk digital printing of different types, we hope to improve predictive models for silk fabric color appearances in design processes and actual printed fabrics. To date, no published research has scientifically characterized color gamut differences in silk printed fabrics and used deep learning methods for color predictions of silk printed fabrics. In this paper, a color prediction method for silk digital printing is proposed based on the Pix2 Pix framework. Color information and fabric style are two important factors in shaping the aesthetic appearance of a fabric.