# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 6 Discussion  
To better satisfy users' esthetic demands and enhance the controllability and esthetic appeal of product image generation, this study is grounded in computational esthetics and visual cognition principles. It employs a product form optimization design methodology that integrates Bezier curves with genetic algorithms to establish a form design model. The esthetic evaluation model is constructed utilizing the esthetic index and entropy weight method. With the optimized design scheme as input, this study utilizes GAN techniques for conducting style transfer experiments, with a particular focus on the car front face as the subject of investigation. The primary contributions and accomplishments of this study are outlined as follows:  
Figure 11: Style experiment contrast: (**a**) style image for Group 1, (**b**) generated image for Group 1, (**c**) style image for Group 2, (**d**) generated image for Group 2. By integrating Bezier curves with genetic algorithms, the parameters of key points in car front-face form samples are extracted. The esthetic index serves as the fitness function to assess the outcomes of genetic operations. Bezier curves, owing to their distinctive form, can distinctly discern curvature changes in forms, whereas genetic algorithms possess robust parameter optimization capabilities for extracting the most representative features from intricate data. This methodology obviates the need for designers to invest considerable time in extensive model training. Commencing directly from form parameters, it minimizes human factor interference and ensures the objectivity of the evaluation process. This approach offers designers an abundance of product form designs and facilitates the swift development of diverse product form solutions. 2. Product form design is conducted on the basis of esthetic index calculation. The formula for calculating the esthetic index determines the esthetic index value of experimental samples, while the entropy weight method is employed to ascertain the weights of the indices and obtain evidence for objective esthetic evaluation, thereby achieving esthetic evaluation of product form. In contrast to traditional manual evaluation methods utilized in intelligent product form design, this methodology utilizes computational esthetics technology to select solutions, yielding superior objectivity and reliability. 3. We apply a GAN to generate realistic images of car front-face outlines. A dataset, comprising paired outline images and their corresponding real images, is created to facilitate the network's learning and mapping processes in complex feature spaces. The GAN meticulously examines both the input form scheme image and the real image, identifying structural proportions and intricate details, thereby enabling it to train effectively and generate realistic images of car front faces. In comparison to previous product outline optimization designs, this methodology attains more realistic image effects that align more closely with actual requirements. Furthermore, when compared to traditional style transfer methods, this approach offers superior controllability over the generated content images. This experiment also has certain limitations. The generated content partially loses information from the edge contour image, particularly the outer contour form of the car front face, leading to incomplete alignment between the generated car front face and the input contour image. The overall proportions of the car front face are therefore incompatible. Clearer and more contour-accurate images were not produced, and the reasons for this can be analyzed from multiple perspectives:  
1. Dataset: Firstly, the training of the GAN necessitates a substantial amount of high-quality data, and the limited experimental data constitutes a primary influencing factor. Furthermore, inconsistencies may exist within the dataset. Secondly, the distribution of car front-face data is intricate, and training the GAN requires intricate parameter adjustments.