复杂光照下同时增强图像并抑制噪声，效果优于现有方法。
Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions
- 分步处理光照与反射层，分别优化亮度和细节。
- 在真实数据集上实现对比度提升与噪声抑制双重优势。
- 适合户外、夜景等复杂光照场景的图像修复任务。
在挑战性光照条件下，捕获的图像常因多种退化因素导致视觉性能下降。尽管已有众多图像增强方法，但往往显著放大固有噪声或仅适用于特定光照环境。为此，本文提出一种新型框架，实现复杂光照下的同步增强与噪声抑制。首先，采用梯度域加权引导滤波（GDWGIF）精确估计光照并改善图像质量；其次，应用Retinex模型将图像分解为独立的光照与反射层，进行并行处理：光照层被修正以优化照明条件，反射层则被增强以提升画质；最后，通过多曝光融合与线性拉伸策略优化图像动态范围。该方法在实际应用中获取的真实世界数据集上进行评估，实验结果表明，在对比度增强与噪声抑制方面均优于当前最优方法。
原文摘要 · Abstract (English)
Under challenging light conditions, captured images often suffer from various degradations, leading to a decline in the performance of vision-based applications. Although numerous methods have been proposed to enhance image quality, they either significantly amplify inherent noise or are only effective under specific illumination conditions. To address these issues, we propose a novel framework for simultaneous enhancement and noise suppression under complex illumination conditions. Firstly, a gradient-domain weighted guided filter (GDWGIF) is employed to accurately estimate illumination and improve image quality. Next, the Retinex model is applied to decompose the captured image into separate illumination and reflection layers. These layers undergo parallel processing, with the illumination layer being corrected to optimize lighting conditions and the reflection layer enhanced to improve image quality. Finally, the dynamic range of the image is optimized through multi-exposure fusion and a linear stretching strategy. The proposed method is evaluated on real-world datasets obtained from practical applications. Experimental results demonstrate that our proposed method achieves better performance compared to state-of-the-art methods in both contrast enhancement and noise suppression.
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