Performance Evaluation of Multimodal Image Generation Algorithm Based on Deep Learning

With the rapid development of deep learning technology, multimodal image generation (MMIG) has become an important research direction in the field of computer vision. In practical applications, the quality of generated images and the consistency between modalities play a crucial role in the effectiveness of the generated model. The problems of poor structural similarity index and peak signal-to-noise ratio in traditional MMIG methods limit their application in complex scenes. This article aims to propose an improved MMIG algorithm based on Generative Adversarial Networks (GANs) to enhance the quality of generated images and consistency between modalities. This study adopts the framework of generative adversarial networks and designs a multimodal generator and discriminator to achieve joint modeling of text and image data. The generator integrates information from different modalities and uses a new loss function to strengthen the relationships between modalities, thereby generating more realistic and diverse images. The experimental results showed that the structural similarity index and peak signal-to-noise ratio of the generated images were significantly improved, with a maximum value of 0.8 for the former and an average value of 41.37 dB for the latter. This study provides an effective solution for MMIG.