通过分离光照与纹理，实现暗光图像的精准增强与超分辨率。
GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
- 分两路处理：先估光照分布，再用光照图引导纹理修复。
- 在OmniNormal5/15数据集上，指标和视觉效果均优于现有方法。
- 适合需要细节保留的暗光图像增强场景。
低光图像超分辨率（LLSR）因分辨率低和光照差的耦合退化而极具挑战。为此，我们提出引导纹理与特征调制网络（GTFMN），将LLSR任务解耦为光照估计与纹理恢复两个子问题。首先，网络设计专用光照分支，用于预测空间变化的光照图，精确捕捉光照分布；随后，该光照图作为显式引导，在新型光照引导调制块（IGM Block）中动态调制纹理分支的特征，实现空间自适应修复，使暗区增强更显著，亮区细节得以保留。大量实验表明，GTFMN在OmniNormal5和OmniNormal15数据集上均优于现有方法，无论在量化指标还是视觉质量上均表现最佳。
原文摘要 · Abstract (English)
Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality.
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