I need a compact, install-ready software layer that plugs into my existing endoscopy unit and upgrades its imaging pipeline. The core of the brief is image-processing: sharper live visuals, instantaneous frame-by-frame analysis, and on-screen flags whenever the algorithm spots a potential anomaly. All processing must happen in real time without introducing perceptible latency to the surgeon’s view. My current hardware outputs standard HDMI and records to DICOM, so your code should sit either between the camera head and the display (FPGA, GPU box, or high-performance PC is fine) or run as a software module on the workstation already attached to the scope. OpenCV, CUDA, TensorFlow, or similarly robust libraries are welcome—just keep licensing constraints clear. Deliverables • Executable or deployable source that enhances image clarity, performs real-time analysis, and triggers automated anomaly detection. • API or integration hooks so I can feed the processed stream back to my recording software. • A concise user guide (video or PDF) so the clinical staff can start using it with minimal training. • Quick-start script or installer plus a short readme covering hardware requirements and GPU driver versions. Acceptance criteria • Latency under 40 ms end-to-end on 1080p60 input. • At least 20 % measurable improvement in contrast-to-noise ratio compared to raw feed (I’ll run side-by-side tests). • Anomaly detection hit rate ≥90 % on my validation clip set with <5 % false positives. If you’ve built vision pipelines for medical or industrial cameras before, you’ll find this straightforward. Share a short outline of your approach, toolchain, and any validation you can demonstrate, and let’s get started.