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arXiv · 1904.05939

Learning Digital Camera Pipeline for Extreme Low-Light Imaging

Syed Waqas Zamir·Aditya Arora·Salman Khan·Fahad Shahbaz Khan·Ling Shao
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Abstract

In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR). We present a data-driven approach that learns the desired properties of well-exposed images and reflects them in images that are captured in extremely low ambient light environments, thereby significantly improving the visual quality of these low-light images. We propose a new loss function that exploits the characteristics of both pixel-wise and perceptual metrics, enabling our deep neural network to learn the camera processing pipeline to transform the short-exposure, low-light RAW sensor data to well-exposed sRGB images. The results show that our method outperforms the state-of-the-art according to psychophysical tests as well as pixel-wise standard metrics and recent learning-based perceptual image quality measures.

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Syed Waqas Zamir, Aditya Arora, Salman Khan, Fahad Shahbaz Khan, Ling Shao. 2019-04-11. Learning Digital Camera Pipeline for Extreme Low-Light Imaging. https://arxiv.org/abs/1904.05939

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