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<p>The primary goal of image dehazing is to restore the clarity and detail of hazy images. However, addressing non-uniform haze in atmospheric scattering models remains a significant challenge. While some methods tackle image-level non-uniformity using multi-scale fusion mechanisms, others focus on feature-level non-uniformity with content-guided attention mechanisms. However, few approaches effectively address non-uniform haze at both the image and feature levels simultaneously. To overcome this limitation, this paper introduces a novel Adaptive Multi-Scale Frequency Selection (AMFS) module, which consists of an Adaptive Multi-Scale Module (AMSM) and a Frequency Selection Block (FSB). The AMSM dynamically integrates multi-scale features through weighted fusion, effectively mitigating issues caused by non-uniform dehazing. Meanwhile, the FSB processes features in the frequency domain, highlighting critical high-frequency and low-frequency components via an attention mechanism, thereby enhancing detail preservation and suppressing noise. Additionally, we propose a Multi-Scale Contrast Regularization (MSCR) loss function, which leverages cross-scale contrastive learning to improve feature consistency. Experimental results demonstrate that the proposed algorithm outperforms existing methods on four benchmark datasets, achieving superior detail preservation and enhanced robustness against non-uniform haze.</p>

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MCRFS-Net: single image dehazing based on multi-scale contrastive regularization and frequency selection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Qin Qin,
  • Lin Shui,
  • Yanyan Zhang,
  • Shaojing Song,
  • Jinhua Jiang

摘要

The primary goal of image dehazing is to restore the clarity and detail of hazy images. However, addressing non-uniform haze in atmospheric scattering models remains a significant challenge. While some methods tackle image-level non-uniformity using multi-scale fusion mechanisms, others focus on feature-level non-uniformity with content-guided attention mechanisms. However, few approaches effectively address non-uniform haze at both the image and feature levels simultaneously. To overcome this limitation, this paper introduces a novel Adaptive Multi-Scale Frequency Selection (AMFS) module, which consists of an Adaptive Multi-Scale Module (AMSM) and a Frequency Selection Block (FSB). The AMSM dynamically integrates multi-scale features through weighted fusion, effectively mitigating issues caused by non-uniform dehazing. Meanwhile, the FSB processes features in the frequency domain, highlighting critical high-frequency and low-frequency components via an attention mechanism, thereby enhancing detail preservation and suppressing noise. Additionally, we propose a Multi-Scale Contrast Regularization (MSCR) loss function, which leverages cross-scale contrastive learning to improve feature consistency. Experimental results demonstrate that the proposed algorithm outperforms existing methods on four benchmark datasets, achieving superior detail preservation and enhanced robustness against non-uniform haze.