Abstract
Image Signal Processor (ISP) plays a core rule in camera systems. However, ISP tuning is highly complicated and requires professional skills and advanced imaging experiences. To skip the painful ISP tuning process, we introduce EEDNet in this paper, which directly transforms an image in the raw space to an image in the sRGB space (RAW-to-RGB). Data-driven RAW-to-RGB mapping is a grand new low-level vision task. In this work, we propose a hypothesis of the receptive field that large receptive field (LRF) is essential in high-level computer vision tasks, but not crucial in low-level pixel-to-pixel tasks. Besides, we present a ClipL1 loss, which simultaneously considers easy examples and outliers during the optimization process. Benefiting from the LRF hypothesis and ClipL1 loss, EEDNet can generate high-quality pictures with more details. Our method achieves promising results on Zurich RAW2RGB (ZRR) dataset and won the first place in AIM2020 ISP challenging.
Y. Zhu, Z. Guo, T. Liang, X. He—Equal Contribution
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Acknowledgements
This work was supported by the Advance Research Program (31511130301); National Key Research and Development Program (2017YFF0209806), and National Natural Science Foundation of China (No. 61906193; No. 61906195; No. 61702510).
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Zhu, Y. et al. (2020). EEDNet: Enhanced Encoder-Decoder Network for AutoISP. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12537. Springer, Cham. https://doi.org/10.1007/978-3-030-67070-2_10
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