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You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

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arxiv 2205.14871 v4 pith:EMHCGUFR submitted 2022-05-30 cs.CV

Ziteng Cui , Kunchang Li , Lin Gu , Shenghan Su , Peng Gao , Zhengkai Jiang , Yu Qiao , Tatsuya Harada This is my paper
classification cs.CV
keywords correctionimageconditionslightlow-lightonlyparametersenhancement
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Challenging illumination conditions (low-light, under-exposure and over-exposure) in the real world not only cast an unpleasant visual appearance but also taint the computer vision tasks. After camera captures the raw-RGB data, it renders standard sRGB images with image signal processor (ISP). By decomposing ISP pipeline into local and global image components, we propose a lightweight fast Illumination Adaptive Transformer (IAT) to restore the normal lit sRGB image from either low-light or under/over-exposure conditions. Specifically, IAT uses attention queries to represent and adjust the ISP-related parameters such as colour correction, gamma correction. With only ~90k parameters and ~0.004s processing speed, our IAT consistently achieves superior performance over SOTA on the current benchmark low-light enhancement and exposure correction datasets. Competitive experimental performance also demonstrates that our IAT significantly enhances object detection and semantic segmentation tasks under various light conditions. Training code and pretrained model is available at https://github.com/cuiziteng/Illumination-Adaptive-Transformer.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning

    cs.CV 2025-11 reject novelty 6.0 of 10

    INTSD, a 6,004-image Indian nighttime traffic-sign dataset, is introduced with LENS-Net, which reports 92.56 mAP@50 detection and 78.89 macro-precision classification, but the paper's internal statistics are inconsistent.

  2. Robust Low-light Scene Restoration via Illumination Transition

    cs.CV 2025-07 conditional novelty 6.0 of 10

    RoSe restores normal-light novel views from low-light multiview images by learning a multiview-consistent illuminance transition field with low-rank denoising.

  3. UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A unified detection-plus-restoration model with learnable DCT frequency gating reports modest mAP gains over multi-task baselines across rain, fog, snow, and low-light tests, with weaker quantitative support for unsee...

  4. WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems

    cs.CV 2025-05 conditional novelty 4.0 of 10

    WTEFNet combines a low-light enhancement module, wavelet feature extraction, and adaptive fusion to improve nighttime object detection on BDD100K, SHIFT, nuScenes, and its own GSN dataset.

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