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<p>Underwater image restoration remains challenging due to complex light propagation and scattering effects in aqueous environments. While recent transformer-based methods have shown promising results in image restoration tasks, they often struggle with the domain gap between underwater and air-captured images and lack explicit mechanisms to handle underwater-specific degradations. This paper presents <i>AsymCT-UIR</i>, a novel asymmetric calibrated transformer network specifically designed for underwater image restoration. Unlike conventional symmetric architectures, our model employs distinct processing pathways: an encoder with learnable calibration modules for underwater-to-air domain adaptation and a decoder with dual attention transformers for feature restoration. The asymmetric design enables effective distortion correction while preserving fine details through adaptive feature calibration and efficient attention mechanisms. Extensive experiments on benchmark datasets demonstrate that our AsymCT-UIR achieves superior performance compared to state-of-the-art methods. The proposed method shows robust performance across various underwater conditions and degradation types, making it practical for real-world applications.</p>

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An asymmetric calibrated transformer network for underwater image restoration

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Xiaojiao Guo,
  • Shenghong Luo,
  • Yihang Dong,
  • Zexiao Liang,
  • Zimeng Li,
  • Xiujun Zhang,
  • Xuhang Chen

摘要

Underwater image restoration remains challenging due to complex light propagation and scattering effects in aqueous environments. While recent transformer-based methods have shown promising results in image restoration tasks, they often struggle with the domain gap between underwater and air-captured images and lack explicit mechanisms to handle underwater-specific degradations. This paper presents AsymCT-UIR, a novel asymmetric calibrated transformer network specifically designed for underwater image restoration. Unlike conventional symmetric architectures, our model employs distinct processing pathways: an encoder with learnable calibration modules for underwater-to-air domain adaptation and a decoder with dual attention transformers for feature restoration. The asymmetric design enables effective distortion correction while preserving fine details through adaptive feature calibration and efficient attention mechanisms. Extensive experiments on benchmark datasets demonstrate that our AsymCT-UIR achieves superior performance compared to state-of-the-art methods. The proposed method shows robust performance across various underwater conditions and degradation types, making it practical for real-world applications.