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Chobola, T. ; Liu, Y.* ; Zhang, H. ; Schnabel, J.A. ; Peng, T.

Fast context-based low-light image enhancement via neural implicit representations.

In: (Computer Vision – ECCV 2024). Berlin [u.a.]: Springer, 2025. 413-430 (Lect. Notes Comput. Sc. ; 15144)
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Low-light image; Illumination estimation; Neural implicit representation
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Computer Vision – ECCV 2024
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15144, Heft: , Seiten: 413-430 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Lung Health and Immunity (LHI)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
Forschungsfeld(er) Enabling and Novel Technologies
Lung Research
PSP-Element(e) G-530006-001
G-505000-001
G-507100-001
Förderungen Helmholtz Association under the joint research school "Munich School for Data Science -MUDS"
DOI 10.1007/978-3-031-73016-0_24
WOS ID 001352825800024
Scopus ID 105018233995
Erfassungsdatum 2024-12-10
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