Super Resolution Poster Generation Model Based on Adversarial Generative Network
DOI:
https://doi.org/10.53469/jtpms.2025.05(6).10Keywords:
Text image generation, Adversarial generative networks, Deep learning, Image GenerationAbstract
With the continuous advancement of computer technology, image generation technology is also developing rapidly. This article proposes a poster generation method using Chinese text as input, which improves the second stage of the StackGan model to a super-resolution model (GAN_SR3). We introduce the CLIP model to map Chinese text to high-dimensional encoding, improving the matching degree between text and image. By inputting encoding and random noise in the first stage of StackGan, low resolution images are generated and then fed into the improved super-resolution model SR3 to obtain high-resolution posters. Compared with other models on the self built dataset, the experimental results showed that GAN_SR3 achieved higher Inception Score and smaller FID on the movie poster and online poster datasets.
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