arXiv cs.AI· Yicui Shi, Yuhan Chen, Xiangfei Huang, Zhenguo Wang, Wenxuan Yu, Ying Fang, Jie Li, Guofa Li·· 4 小时前AI 评分34
CueDR-Net：基于线索引导双路径重建与自适应多尺度响应精炼的低光照图像增强方法
Cue-guided dual-path reconstruction with adaptive multi-scale response refinement for low-light image enhancement
AI 导读
论文提出 CueDR-Net，一种双路径低光照图像增强网络，通过亮度调整与局部内容重建两条路径及双向线索交互，缓解现有方法易出现的色彩偏差和细节模糊问题。其 AMRRConv 卷积用像素级权重融合可学习局部过渡响应与多尺度特征，捕捉跨尺度弱结构。
正文 · 原文
Abstract:Low-light image enhancement (LLIE) supports optical sensing and scene observation when limited photon capture and sensor noise degrade image quality. However, existing methods can introduce colour deviations or blur local details in the enhanced images. To address this challenge, we propose CueDR-Net, a dual-path network that combines brightness adjustment with local content reconstruction through bidirectional cue interaction and adaptive multi-scale refinement. Specifically, a fixed extractor derives luminance variation, colour, and texture cue maps from the input image. These maps are encoded in a cue stream that exchanges features with the image stream through bidirectional attention. The exchanged features support two paths for brightness adjustment and local content reconstruction. The brightness adjustment path predicts pixel wise adjustment maps that control iterative intensity changes in the input image. In parallel, the residual path uses the exchanged features to predict an additive correction for local content reconstruction. To capture weak structures across scales, Adaptive Multi-Scale Response Refinement Convolution (AMRRConv) refines the features used for both predictions. It fuses learnable local transition responses with multi-scale features from factorized three-plane filtering using pixel wise weights. The adjusted image is added to the correction before being mapped to the enhanced image. Evaluations on LOLv1, LSRW-HUAWEI, and LSRW-NIKON demonstrate competitive enhancement performance, supported by ablation studies. AMRRConv also achieves competitive low-light detection performance with a small neck parameter count when integrated into YOLOv11.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.16482 [cs.CV] |
| (or arXiv:2603.16482v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.16482 arXiv-issued DOI via DataCite |
Submission history
From: Yuhan Chen [view email]
[v1]
Tue, 17 Mar 2026 13:07:32 UTC (38,291 KB)
[v2]
Wed, 4 Nov 2026 17:23:39 UTC (38,249 KB)
来源：arXiv cs.AI · arxiv.org