针对夜间雾霾图像，提出基于直方图引导的融合与频域自适应优化方法。
HistoFusionNet: Histogram-Guided Fusion and Frequency-Adaptive Refinement for Nighttime Image Dehazing
- 利用直方图分组建模长程依赖，聚焦相似退化区域特征
- 频域自适应分支融合高低频信息，提升结构恢复与细节增强
- 在NTIRE 2026挑战赛中排名第一，适用于复杂夜间退化场景
夜间图像去雾仍是低层视觉难题，因雾霾、光晕、光照不均、色彩失真及传感器噪声共存，常使日间去雾假设失效。为此，我们提出HistoFusionNet，一种基于Transformer增强的架构，结合直方图引导表征学习与频域自适应特征精炼。该方法在多尺度编码-解码主干上引入直方图变换块，按动态范围特征分组特征，有效聚合复杂夜间光照下的相似退化区域。为进一步提升重建保真度，设计频率感知精炼分支，自适应融合互补的低频与高频线索，有助于恢复场景结构、抑制伪影并增强局部细节。该统一框架特别适合真实夜间雾霾场景中的异质退化。大量实验表明，本方法在NTIRE 2026夜间图像去雾挑战赛中表现优异，团队位列22支参赛队伍首位，验证了HistoFusionNet的鲁棒性与竞争力。代码已公开：https://github.com/heydarimo/Night-Time-Dehazing
原文摘要 · Abstract (English)
Nighttime image dehazing remains a challenging low-level vision problem due to the joint presence of haze, glow, non-uniform illumination, color distortion, and sensor noise, which often invalidate assumptions commonly used in daytime dehazing. To address these challenges, we propose HistoFusionNet, a transformer-enhanced architecture tailored for nighttime image dehazing by combining histogram-guided representation learning with frequency-adaptive feature refinement. Built upon a multi-scale encoder-decoder backbone, our method introduces histogram transformer blocks that model long-range dependencies by grouping features according to their dynamic-range characteristics, enabling more effective aggregation of similarly degraded regions under complex nighttime lighting. To further improve restoration fidelity, we incorporate a frequency-aware refinement branch that adaptively exploits complementary low- and high-frequency cues, helping recover scene structures, suppress artifacts, and enhance local details. This design yields a unified framework that is particularly well suited to the heterogeneous degradations encountered in real nighttime hazy scenes. Extensive experiments and highly competitive performance of our method on the NTIRE 2026 Nighttime Image Dehazing Challenge benchmark demonstrate the effectiveness of the proposed method. Our team ranked 1st among 22 participating teams, highlighting the robustness and competitive performance of HistoFusionNet. The code is available at: https://github.com/heydarimo/Night-Time-Dehazing
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