title: Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism
    via Dual Diffusion Models and GPT Prompting
summary: Sonar image synthesis is crucial for advancing applications in underwater
  exploration, marine biology, and defence. Traditional methods often rely on
  extensive and costly data collection using sonar sensors, jeopardizing data
  quality and diversity. To overcome these limitations, this study proposes a new
  sonar image synthesis framework, Synth-SONAR leveraging diffusion models and
  GPT prompting. The key novelties of Synth-SONAR are threefold: First, by
  integrating Generative AI-based style injection techniques along with publicly
  available real/simulated data, thereby producing one of the largest sonar data
  corpus for sonar research. Second, a dual text-conditioning sonar diffusion
  model hierarchy synthesizes coarse and fine-grained sonar images with enhanced
  quality and diversity. Third, high-level (coarse) and low-level (detailed)
  text-based sonar generation methods leverage advanced semantic information
  available in visual language models (VLMs) and GPT-prompting. During inference,
  the method generates diverse and realistic sonar images from textual prompts,
  bridging the gap between textual descriptions and sonar image generation. This
  marks the application of GPT-prompting in sonar imagery for the first time, to
  the best of our knowledge. Synth-SONAR achieves state-of-the-art results in
  producing high-quality synthetic sonar datasets, significantly enhancing their
  diversity and realism.
paper_authors._id: 670d5b7e30db04d0077a739a
paper_authors.name: Purushothaman Natarajan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T18:59:42.551Z
paper_authors._id: 65dc5e2315c63a1468d98e8a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65dc5e2315c63a1468d98e8a/1TFfFTM80SlRn5bFA0DGP.png
paper_authors.fullname: Purushothaman Natarajan
paper_authors.type: user
paper_authors.user: Purushothamann
paper_authors._id: 670d5b7e30db04d0077a739b
paper_authors.name: Kamal Basha
paper_authors._id: 670d5b7e30db04d0077a739c
paper_authors.name: Athira Nambiar
paper_title: Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism
    via Dual Diffusion Models and GPT Prompting
paper_summary: Sonar image synthesis is crucial for advancing applications in underwater
  exploration, marine biology, and defence. Traditional methods often rely on
  extensive and costly data collection using sonar sensors, jeopardizing data
  quality and diversity. To overcome these limitations, this study proposes a new
  sonar image synthesis framework, Synth-SONAR leveraging diffusion models and
  GPT prompting. The key novelties of Synth-SONAR are threefold: First, by
  integrating Generative AI-based style injection techniques along with publicly
  available real/simulated data, thereby producing one of the largest sonar data
  corpus for sonar research. Second, a dual text-conditioning sonar diffusion
  model hierarchy synthesizes coarse and fine-grained sonar images with enhanced
  quality and diversity. Third, high-level (coarse) and low-level (detailed)
  text-based sonar generation methods leverage advanced semantic information
  available in visual language models (VLMs) and GPT-prompting. During inference,
  the method generates diverse and realistic sonar images from textual prompts,
  bridging the gap between textual descriptions and sonar image generation. This
  marks the application of GPT-prompting in sonar imagery for the first time, to
  the best of our knowledge. Synth-SONAR achieves state-of-the-art results in
  producing high-quality synthetic sonar datasets, significantly enhancing their
  diversity and realism.
paper_ai_summary: Synth-SONAR uses diffusion models and GPT prompting to generate high-quality and diverse sonar images from text descriptions, advancing sonar image synthesis.
paper_ai_keywords: diffusion models GPT prompting Generative AI style injection real/simulated data dual text-conditioning sonar diffusion model high-level sonar generation low-level sonar generation visual language models textual prompts