R&D

R&D

Real-Time Video Dehazing on Edge Devices

To build an end-to-end system for real-time image dehazing, with model inference happening on a self-hosted API and video processed on an edge device.

Timeline

2-3 months

Team

2-3 developers

Budget

$50,000

Results

1

R

The Challenge

Real-time video processing is computationally expensive. The challenge was to create a dehazing model that was both accurate and efficient enough to run on low-power edge devices like a Raspberry Pi.

Our Solution

To build an end-to-end system for real-time image dehazing, with model inference happening on a self-hosted API and video processed on an edge device.

Technologies Used

TensorFlowOpenCVRaspberry PiWebSocketPython

Key Results

Proved capability in building complex, end-to-end AI systems that span from cloud infrastructure to edge computing.

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