UniBlendNet：统一的全局、多尺度和区域自适应建模用于环境光照归一化
UniBlendNet: Unified Global, Multi-Scale, and Region-Adaptive Modeling for Ambient Lighting Normalization
AI总结：
UniBlendNet通过统一建模全局光照、多尺度结构和区域自适应细化，提升复杂光照条件下的图像恢复性能，实验表明其优于IFBlend。
摘要：
环境光照归一化（ALN）旨在恢复受复杂、空间变化光照条件退化的图像。现有方法如IFBlend利用频域先验建模光照变化，但存在全局上下文建模有限和空间适应性不足的问题，导致在挑战区域恢复效果不佳。本文提出UniBlendNet，一个统一的环境光照归一化框架，联合建模全局光照、多尺度结构和区域自适应细化。具体而言，我们通过集成基于UniConvNet的模块增强全局光照理解，以捕捉长距离依赖。为更好地处理复杂光照变化，我们引入了Scale-Aware Aggregation Module（SAAM），通过基于金字塔的多尺度特征聚合与动态重加权。此外，我们设计了掩码引导的残差细化机制，以实现区域自适应修正，使模型能够选择性地增强退化区域，同时保持良好曝光区域。该设计在复杂光照条件下有效提升了光照一致性和结构保真度。在NTIRE环境光照归一化基准上的大量实验表明，UniBlendNet在性能上一致优于基线IFBlend，实现了改进的恢复质量，同时产生更自然和稳定的恢复结果。
英文摘要：
Ambient Lighting Normalization (ALN) aims to restore images degraded by complex, spatially varying illumination conditions. Existing methods, such as IFBlend, leverage frequency-domain priors to model illumination variations, but still suffer from limited global context modeling and insufficient spatial adaptivity, leading to suboptimal restoration in challenging regions. In this paper, we propose UniBlendNet, a unified framework for ambient lighting normalization that jointly models global illumination, multi-scale structures, and region-adaptive refinement. Specifically, we enhance global illumination understanding by integrating a UniConvNet-based module to capture long-range dependencies. To better handle complex lighting variations, we introduce a Scale-Aware Aggregation Module (SAAM) that performs pyramid-based multi-scale feature aggregation with dynamic reweighting. Furthermore, we design a mask-guided residual refinement mechanism to enable region-adaptive correction, allowing the model to selectively enhance degraded regions while preserving well-exposed areas. This design effectively improves illumination consistency and structural fidelity under complex lighting conditions. Extensive experiments on the NTIRE Ambient Lighting Normalization benchmark demonstrate that UniBlendNet consistently outperforms the baseline IFBlend and achieves improved restoration quality, while producing visually more natural and stable restoration results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
