Real-time Image Processing for 400 fps 2.7 MPix camera with 10-GigE

Updated: August 27, 2026

Image processing for camera applications has one very essential feature: it should not allow frame drops, all image processing should be done at real time. This is exactly what we can offer for high-speed and high data rate cameras which generate huge streams. With GPU-based ISP we can even perform long-term recording with maximun performance.

How these numbers were measured. Fastvideo benchmarks are measured on NVIDIA GPUs, timing pure GPU processing at the resolution, bit depth, format and quality stated on this page. See the full benchmark methodology and test environment and the Fastvideo SDK benchmark report (PDF). Our tools are open source on GitHub and built on NVIDIA CUDA.

Check a single module. Every module in the Fastvideo SDK comes with a command-line sample. Run it on your own images on your own GPU and compare the result with the figures published here.

Check the whole pipeline. A module in the middle of a pipeline cannot be judged on its own: its input is hard to prepare, and its output is an intermediate product you cannot look at. What matters is how it changes the final image and the total time of the schema. Two of our applications run the complete pipeline and report the time of every stage — FastVCR (live cameras or RAW files from disk, so no camera is required) and Fast CinemaDNG (DNG, offline). The benchmark module is included in the free builds of both.

Detailed benchmark data. Measured results are published on our benchmark pages: all benchmarks.

We wrote these algorithms ourselves

Image processing has always been a trade-off between quality and speed. The methods that give a good result take minutes per frame; the fast ones leave halos and artifacts. The reason is the compute budget: on a CPU there was never enough of it, so algorithms were simplified — and much of the industry still runs on simplifications accepted decades ago.

Fastvideo’s codecs and ISP modules are neither ports of that legacy nor wrappers around third-party code. We designed — and in many cases invented — the algorithms behind our demosaicing, denoising, tone mapping and JPEG/JPEG2000 codecs, and wrote them for CUDA from the start, on top of image-processing expertise built since 2009. An algorithm moved to the GPU still carries the compromises it was simplified for; ours were never made under that constraint. On a modern GPU the budget is large enough that, for practical workloads, the choice between quality and speed no longer has to be made.

That is why you get a complete, tuned imaging solution rather than a bag of GPU primitives — and why, when your pipeline needs something specific, we can change the algorithm itself, not just call it.

Camera Features

IMX421 image sensor: 1936 × 1472 pixels, 4.5µm × 4.5µm
Global Shutter CMOS
Bit depth: 8/10/12-bit
Frame rate up to 409 fps (8-bit)
Exposure time 7µs-10s
Analog Gain 0-24dB
Digital Gain 0.004-8.000x
Data Rate 1165 MB/s (8-bit)
10-GigE interface
Dynamic Range =72 dB
Optical Size: 10.94mm
Lens interface: C-mount
Temperature monitor
Power Supply DC12-24V(±10%) 2.5A
Power Consumption: 16.0 W
Dimensions 70.0mm(W) × 70.0mm(H) × 73.2mm(D)(Without adapter)

Image processing pipeline on GPU

How do you process a 400 fps camera stream in real time?

Image acquisition
Frame unpacking for 10-bit and 12-bit modes
Image linearization
Dark frame subtraction (FPN)
Flat-Field Correction (shading correction)
Bad pixel removal
Adaptive Exposure and Gain control
Exposure correction (brightness control)
Denoiser: Bilateral, NLM
Rotation to 90/180/270 degrees and flip/flop
Crop
Resize (downscale and upscale)
Rotation to an arbitrary angle
Undistortion via LCP or via calibrated maps
Sharpening
Gamma transform
JPEG compression and storage on SSD

Available software outputs

Video output to monitor via OpenGL in real time
Camera statistics
Real-time processing and JPEG compression with image storage on SSD

Vision System Components

10-GigE monochrome camera 2.7 MPix at 380 fps
External PCIe Gen3 x4 frame grabber with 4 x SFP+, compatible with optical/electrical ports
Lens with C-mount
Cable to connect camera and grabber
PC with installed NVIDIA GPU, Windows/Linux
Tripod

About the author

Fyodor Serzhenko, PhD, is the founder and CEO of Fastvideo. He earned his PhD at the Moscow Institute of Physics and Technology (MIPT) in 1993. Since 2009 he has led the development of Fastvideo’s GPU-accelerated image codecs and ISP modules, including the Fastvideo SDK. Connect on LinkedIn.

Why you can trust these results

Fastvideo has built GPU-accelerated image processing software since 2009. Our codecs comply with the standards they implement — JPEG (ITU-T T.81 / ISO IEC 10918) and JPEG2000 (ITU-T T.800 / ISO IEC 15444) — and our tools are open source on GitHub. Every performance figure we publish is reproducible: see our benchmark methodology, download the Fastvideo SDK benchmark report (PDF), and measure it on your own GPU.