Synth-SONAR: Sonar Image Synthesis with Enhanced Diversity and Realism
  via Dual Diffusion Models and GPT Prompting
Authors: name: Purushothaman Natarajan, name: Kamal Basha, name: Athira Nambiar

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.