# Product Image Generation Method Based on Morphological Optimization and Image Style Transfer
## 7 Conclusions  
In this study, a morphological scheme diagram and a reference image of a car's front end were utilized as inputs, with dual encoders employed to extract both style and content features. During the decoding phase, a conditional skip connection was incorporated to constrain the morphological scheme diagram, thereby enhancing edge and detail information to ensure the fidelity of the generated images to the original scheme. By incorporating a realism discriminator, the quality of the generated images was further enhanced, aiding designers in effectively boosting their efficiency in selecting effect images. This, in turn, reduced unnecessary steps in the design process and facilitated the rapid and efficient completion of product design representations. Experimental results demonstrate that the proposed model improves the quality of image generation to a significant degree. However, the generated effect images still exhibit issues such as a lack of vibrant color effects and incompatible contour lines. Future research should prioritize the development of models that can compensate for additional texture details. At the same time, the experiment also has a disadvantage: the generated content partially loses the information in the edge contour map, such as the outer contour shape of the front face of the car, resulting in the generated front face of the car not exactly matching the input contour map, and the overall proportion of the front face of the car is incompatible. The lack of images that are clearly closer to the morphology of the contour map may be attributed to the following reasons: Firstly, the training of generative adversarial networks requires a large amount of high-quality data, and the lack of experimental data is one of the main effects, and there may be inconsistencies in the dataset. Secondly, the data distribution of the front face of the car is complex, and the training of the generative adversarial network needs to be adjusted with complex parameters. This study combines the genetic algorithm of style transfer and uses esthetic indicators as fitness functions to pre-optimize product morphological parameters to generate content images with high esthetic quality. Through the GAN, the optimized morphological design scheme is integrated with the target style diagram to generate a car front-face rendering with both structural rationality and an artistic nature. However, this method has the disadvantages of high computational complexity, too long a processing time for images, and insufficient detail fidelity, and the generation of images may lose contour accuracy (such as blurred headlights) or experience color deviation due to style complexity. Genetic algorithms are suitable for various optimization problems, and efficient exploration of the design space is achieved through parameter coding and fitness functions. However, genetic algorithms provide controllable contour schemes at low computational costs and lack visual expressiveness [30]. The style migration GAN model directly generates results based on content images and style images, with a simple process and high computing efficiency. However, it is difficult for this model to actively optimize the morphological structure, and it can only migrate textures and color styles and cannot correct the inherent defects of the content images. Therefore, by integrating these two methods to form complementarity,genetic algorithms provide morphological design solutions, style transfer enhances visual effects, and the two jointly promote the development of intelligent design. Finally, diffusion-based generative frameworks have demonstrated exceptional efficacy in synthesizing high-fidelity imagery conditioned on diverse inputs. Subsequent research will establish a hybrid diffusion architecture to generate product morphological solutions with sub-pixel geometric precision and physically plausible textural characteristics within categorical constraints, effectively overcoming current limitations in spectral fidelity and micro-scale feature delineation.