Wednesday, May 04, 2022

Low Light Video Denoising

A team from UC Berkeley and Intel Labs has posted a new pre-print titled "Dancing under the stars: video denoising in starlight". They present a new method for denoising videos captured in extremely low illumination of fractions of a lux.

Abstract:
Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we demonstrate photorealistic video under starlight (no moon present, <0.001 lux) for the first time. To enable this, we develop a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light levels. Using this noise model, we train a video denoiser using a combination of simulated noisy video clips and real noisy still images. We capture a 5-10 fps video dataset with significant motion at approximately 0.6-0.7 millilux with no active illumination. Comparing against  alternative methods, we achieve improved video quality at the lowest light levels, demonstrating photorealistic video denoising in starlight for the first time.













The data in this paper was captured using an NIR-enhanced RGB camera sensor optimized for low light imaging. The authors write:

We choose to use a Canon LI3030SAI Sensor, which is a 2160x1280 sensor with 19┬Ám pixels, 16 channel analog output, and increased quantum efficiency in NIR. This camera has a Bayer pattern consisting of red, green, blue (RGB), and NIR channels (800-950nm). Each RGB channel has an additional transmittance peak overlapping with the NIR channel to increase light throughput at night. During daylight, the NIR channel can be subtracted from each RGB channel to produce a color image, however at night when NIR is dominant, subtracting out the NIR channel will remove a large portion of the signal resulting in muffled colors. We pair this sensor with a ZEISS Otus 28mm f/1.4 ZF.2 lens, which we choose due to its large aperture and wide field-of-view.

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