Abstract
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: this https URL
Abstract (translated)
夜间图像去雾由于同时存在雾霾、光晕、非均匀照明、色彩失真和传感器噪声等问题，仍是具有挑战性的底层视觉难题，这些因素常使白天去雾中常用的假设失效。为应对这些挑战，我们提出了HistoFusionNet——一种经Transformer增强的架构，通过结合直方图引导的表征学习与频率自适应特征精炼，专门针对夜间图像去雾设计。该方法基于多尺度编码器-解码器主干，引入了直方图Transformer模块，该模块依据动态范围特性对特征进行分组，从而对复杂夜间光照条件下相似退化区域进行更有效的聚合。为进一步提升复原保真度，我们融入了频率感知精炼分支，该分支自适应地利用互补的低频与高频线索，帮助恢复场景结构、抑制伪影并增强局部细节。这一设计形成了一个统一框架，特别适用于真实夜间雾景中遇到的异质退化问题。在NTIRE 2026夜间图像去雾挑战赛基准上的大量实验及极具竞争力的性能表现，验证了所提方法的有效性。我们的团队在22支参赛队伍中排名第一，凸显了HistoFusionNet的鲁棒性与竞争性。代码已公开于：此HTTPS链接。
URL
https://arxiv.org/abs/2604.03800
