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<p>This paper proposes a novel knowledge-guided learning model for low-light image enhancement. The developed pipeline improves image visibility by addressing noise and contrast issues, detail preservation, and color balancing. This is achieved by integrating denoising and Retinex techniques with vision transformers. Given as little as a single low-light input image, a set of intermediate exposures are generated by means of gamma transform, which serve as inputs to an ensemble of ten transformer models to produce enhanced outputs. An adaptive exposure selection process is then applied based on a composite image quality score. Finally, the selected outputs are fused in a multi-scale manner using weight maps based on contrast, saturation, and well-exposedness features. Extensive experiments on benchmark datasets, LOL-v1, LOL-v2-Real, LOL-v2-Synthetic, and a unified dataset, demonstrate that the proposed method is competitive with state-of-the-art techniques and shows a significant advantage when processing images captured in extremely low-light conditions. In addition, the developed method is successfully applied to the image dehazing problem without any further optimization.</p>

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Low-light image enhancement: a multi-stage hybrid approach via Retinex and vision transformers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Sena Yagmur Sen,
  • Mehmet Turkan,
  • Gazihan Alankus

摘要

This paper proposes a novel knowledge-guided learning model for low-light image enhancement. The developed pipeline improves image visibility by addressing noise and contrast issues, detail preservation, and color balancing. This is achieved by integrating denoising and Retinex techniques with vision transformers. Given as little as a single low-light input image, a set of intermediate exposures are generated by means of gamma transform, which serve as inputs to an ensemble of ten transformer models to produce enhanced outputs. An adaptive exposure selection process is then applied based on a composite image quality score. Finally, the selected outputs are fused in a multi-scale manner using weight maps based on contrast, saturation, and well-exposedness features. Extensive experiments on benchmark datasets, LOL-v1, LOL-v2-Real, LOL-v2-Synthetic, and a unified dataset, demonstrate that the proposed method is competitive with state-of-the-art techniques and shows a significant advantage when processing images captured in extremely low-light conditions. In addition, the developed method is successfully applied to the image dehazing problem without any further optimization.