Computational Marine Imagery · v3.1 · Bioluminescent

Reveal the Hidden Depths of the Ocean

Sensor-agnostic AI enhancement for underwater exploration. From murky trenches to sunlit reefs — our diffusion models recover lost information in real time, on the edge.

0.03s
Inference
4×
Super-Resolution
15K+
Species Recognised
98.5%
mAP Detection
About — The Mission

Making the invisible visible

UnderwaterAI is a sensor-agnostic enhancement suite for marine imagery. Drop in a murky photo, a colour-shifted video frame, or a feed from a 4 000 m-rated ROV — we recover detail in real time and tell you what you're looking at.

01

Enhance

Two specialised diffusion models (Enhancement A for edge / mobile, Enhancement B for photorealism) recover contrast, correct colour cast, and super-resolve 4×. Inference in 30 ms on a Jetson Nano.

  • Edge-ready, <15 W
  • INT8 quantised
  • 4× super-resolution
  • Real-time, 30 fps
02

Identify

A YOLO-based detector trained on the Indian Ocean Dataset (IOD), EUVP, and UIEB recognises 15 000+ marine species and flags potential threats — submarines, mines, divers. Confidence-scored and exportable to marine-biology surveys.

  • 15 000+ species classes
  • 98.5% mAP @ 50 ms
  • Threat catalogue
  • Exportable annotations
03

Map

Frame-to-frame stitching produces georeferenced 3-D terrain reconstructions of the seafloor — rock formations, sediment types, hydrothermal vents, and potential petroleum indicators. Native formats for GIS and survey tooling.

  • 3-D point clouds
  • Geo-referenced
  • Sediment classification
  • GIS-export ready
0.03s
Inference time
4×
Super-resolution
15K+
Species catalogued
98.5%
Detection mAP
<15W
Jetson power budget
Founded
2024
Headquartered
India
Stage
Seed · MeitY-funded
Deployment
Edge (Jetson) · Server · ROV
Status
Live · v1.0
01 — Before & After

Experience the transformation

Drag the slider to reveal how our AI recovers lost detail from murky underwater footage — in real-time, on the edge.

Enhancement Model A — High-detail underwater super-resolution

Original unprocessed underwater photograph — murky and colour-shifted AI-enhanced underwater image — Enhancement Model A output MODEL A ENHANCED ORIGINAL
0.03s
Inference Time
4×
Super-Resolution
INT8
Quantised Edge Deploy
<15W
Jetson Power Budget
02 — Our Models

Two specialised enhancement models

Each model is tuned for a different underwater scenario. Compare outputs side-by-side and choose what fits your mission.

Model A
Enhancement Model A: AI-enhanced underwater image showing improved detail and clarity

Enhancement Model A

Optimised for broad underwater conditions with strong edge preservation and detail recovery across varying turbidity. Designed to perform reliably in complex, low-visibility environments.

Task
Super-Resolution
Scale
4× Upscale
Deploy
Edge / Mobile
Best For
General Use
Edge Recovery High Contrast Mobile Ready
Model B
Enhancement Model B: AI-enhanced underwater image with photorealistic colour fidelity

Enhancement Model B

Produces natural, photorealistic results with superior colour fidelity and reduced artefacts. Ideal for scientific documentation, marine-biology surveys, and archival research imagery.

Task
Photo Enhancement
Scale
4× Upscale
Deploy
Server / ROV
Best For
Photorealism
Color Fidelity Natural Output Low Artefacts

Side-by-side comparison

Original unprocessed underwater input image
Original
Unprocessed input
Enhancement Model A output
Model A Output
High-detail enhancement
Enhancement Model B output
Model B Output
Photorealistic enhancement
03 — Architecture

Computational architecture

A dual-phase system combining server-side training with real-time edge deployment for AUVs, ROVs, and diver-headset rigs.

Server Training

UDnet training on unpaired data with synthetic generation for LU2net and MobileIE. CLIP validation ensures quality.

PyTorch RTX GPUs

Edge Deployment

Real-time frame enhancement using MobileIE and LU2net on Jetson Nano / Xavier NX with a sub-15 W power budget.

TensorRT ONNX

Threat Detection

YOLO-based detection for submarines, mines, divers, and marine species with confidence scoring.

YOLO OpenCV

Auto-Retraining

Nightly retraining with fresh ROV data. Automated weight distribution to deployed devices via a central hub.

MLOps CI/CD

CLIP Validation

Vision-language model filters poor training samples and validates data quality before model updates ship to edge.

CLIP Quality

Analytics Dashboard

Real-time metrics: PSNR, SSIM, UIQM quality scores with YOLO confidence visualisation and telemetry.

React WebGL
End-to-end pipeline
Raw Footage Pre-process UDnet / LU2net YOLO Detect Output
04 — Beyond Enhancement

Intelligent detection

Beyond enhancement — our system identifies marine species, geological formations, and potential threats automatically.

Underwater reef scene used to demonstrate AI detection
● Live Detection
3 species identified
Processing: 12 ms · model: YOLOv8-marine
Parrot Fish · 98%
Brain Coral · 95%
Staghorn Coral · 92%

Geological Structures

Rock formations, sediment types, hydrothermal vents, and potential petroleum indicators.

Biodiversity Cataloguing

Automatic species identification for marine biologists and ecological surveys.

Threat Detection

Submarine identification, underwater mines, unauthorised-diver detection for maritime security.

15K+
Species Classes
98.5%
mAP Score
50ms
Latency
05 — Marine Solutions

Elevate your ocean adventures

Whether you're a recreational scuba diver, an underwater photographer, or a marine-tourism operator — UnderwaterAI transforms every dive into a crystal-clear experience.

Scuba Diving

Real-time AI enhancement cuts through murk, green water, and turbidity — giving vivid, true-colour footage on every dive.

Real-time All Depths

Underwater Photography

AI colour restoration brings out the vibrant hues of coral reefs that cameras miss in low-light environments.

Colour Restore RAW Support

Coral Reef Exploration

Species-identification AI recognises 15,000+ marine species in real-time, turning every reef dive into an interactive tour.

Species ID Reef Mapping

Wreck Exploration

Enhanced low-light visibility reveals intricate details of shipwrecks, making technical diving safer and more rewarding.

Low-Light Depth Safe

Tour Operators

Integrate our API into dive-centre cameras and headsets to provide AI-guided tours, species ID, and stunning HD memories.

API White-label

Diver Safety

Real-time object detection identifies hazards, sea creatures, and navigation landmarks — keeping divers safe at depth.

Hazard Detect Nav Aid

Ready to Dive Deeper?

Join thousands of divers, photographers, and marine-tourism operators who have transformed their underwater experience with UnderwaterAI.

06 — Trusted By

Who we serve

Dual-use technology serving defence, research, and industrial applications.

Defence & Security

Maritime surveillance, submarine detection, coastal monitoring, threat identification.

Petrochemical

Underwater pipeline inspection, offshore rig maintenance, exploration site surveys.

Research Institutes

Oceanographic studies, marine biology, climate monitoring, deep-sea exploration.

Scuba & Commercial Divers

Real-time visibility enhancement for diver navigation, wreck exploration, photography.

07 — Leadership

The founders

Our founding leadership team — shaping the future of underwater intelligence.

Gautam Singh

CEO

Chief Executive Officer. Sets the vision and drives strategic partnerships across defence and research.

Shuvam Banerji Seal

CTO

Chief Technology Officer. Leads model architecture, edge deployment, and the engineering team.

Youktik Sajjan

COO

Chief Operating Officer. Orchestrates operations, partnerships, and field deployments.

Aman Kumar

CPO

Chief Product Officer. Shapes the product roadmap, UX, and customer success.

Proudly funded by MeitY · Government of India