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Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15144))

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Abstract

Current deep learning-based low-light image enhancement methods often struggle with high-resolution images, and fail to meet the practical demands of visual perception across diverse and unseen scenarios. In this paper, we introduce a novel approach termed CoLIE, which redefines the enhancement process through mapping the 2D coordinates of an underexposed image to its illumination component, conditioned on local context. We propose a reconstruction of enhanced-light images within the HSV space utilizing an implicit neural function combined with an embedded guided filter, thereby significantly reducing computational overhead. Moreover, we introduce a single image-based training loss function to enhance the model’s adaptability to various scenes, further enhancing its practical applicability. Through rigorous evaluations, we analyze the properties of our proposed framework, demonstrating its superiority in both image quality and scene adaptability. Furthermore, our evaluation extends to applications in downstream tasks within low-light scenarios, underscoring the practical utility of CoLIE. The source code is available at https://github.com/ctom2/colie.

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Acknowledgements

Tomáš Chobola is supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”.

Author information

Authors and Affiliations

  1. School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

    Tomáš Chobola, Yu Liu, Hanyi Zhang, Julia A. Schnabel & Tingying Peng

  2. Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany

    Tomáš Chobola, Hanyi Zhang, Julia A. Schnabel & Tingying Peng

  3. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Julia A. Schnabel

Authors
  1. Tomáš Chobola
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  2. Yu Liu
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  3. Hanyi Zhang
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  4. Julia A. Schnabel
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  5. Tingying Peng
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Corresponding author

Correspondence to Tomáš Chobola .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Chobola, T., Liu, Y., Zhang, H., Schnabel, J.A., Peng, T. (2025). Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15144. Springer, Cham. https://doi.org/10.1007/978-3-031-73016-0_24

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