GPT-prompted sonar image synthesis: A new frontier in underwater data generation.
https://arxiv.org/abs/2410.08612
Original Problem 🔍:
Sonar image synthesis faces challenges in data scarcity, quality, and diversity. Traditional methods rely on costly data collection, limiting research and applications in underwater exploration.
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Solution in this Paper 🛠️:
• Synth-SONAR framework leverages dual diffusion models and GPT prompting
• Creates large dataset by combining real, simulated, and AI-generated images
• Uses two-stage image generation: coarse and fine-grained
• Incorporates GPT and vision-language models for improved text-to-image synthesis
• Applies style injection techniques to enhance image diversity
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Key Insights from this Paper 💡:
• First application of GPT-prompting in sonar imagery generation
• Dual-stage diffusion model hierarchy enhances image quality and diversity
• Integration of language models bridges gap between text descriptions and sonar image generation
• Style injection with attention mechanism improves feature separation in generated images
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Results 📊:
• Outperforms state-of-the-art models in image quality metrics (SSIM: 0.381, PSNR: 12.730, FID: 3.8)
• Achieves up to 97% accuracy in sonar image classification when combining real and synthetic data
• Generates high-quality synthetic sonar images with enhanced diversity and realism
• Enables controlled and interpretable sonar image synthesis through text prompts
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