Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement (ICCV 2025)

Official PyTorch implementation of the paper "Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement", accepted by ICCV 2025.

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🌟 Introduction

Light field (LF) imaging in low-light environments suffers from severe noise and detail loss. While standard enhancement methods focus on individual views, they often break the View Consistency—the fundamental geometric relationship between sub-aperture images (SAIs).

Our method introduces a Scene-Adaptive framework that restores high-quality LF images while strictly preserving their 4D geometric structure, even in extreme darkness.

Key Contributions:

1. Scene-Adaptive Luminance Mapping: Dynamically adjusts enhancement parameters based on global and local illumination statistics.
2. Spatial-Angular Interaction (SAI) Module: Deeply integrates cross-view information to compensate for noise-corrupted pixels.
3. EPI-based Geometric Constraint: A novel loss function targeting Epipolar Plane Images (EPI) to ensure smooth disparity and flicker-free view transitions.

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📝 Citation

If you find our work useful, please cite:

@InProceedings{Zhang_2025_ICCV,
    author    = {Zhang, Shuo and Gao, Chen and Lin, youfang},
    title     = {Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    year      = {2025}
}

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📧 Contact

For any questions, please contact Shuo Zhang at [zhangshuo@bjtu.edu.cn]. More research from our lab can be found at INSIS-CV Page.

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