用物理先验训练无配对去雾模型，提升细节与色彩还原
UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior
- 基于迭代一致性先验估计，融合光照与反照率信息
- 动态小波卷积保留高低频特征，细节更清晰
- 自监控机制消除噪声，适合真实雾霾图像修复
图像去雾旨在增强对比度并恢复细节，对保持视觉信息和提升图像处理精度至关重要。现有方法依赖单一人工先验，难以有效揭示图像细节。为此，我们提出一种无配对图像去雾网络——基于无配对丰富物理先验的简单去雾增强器（UR2P-Dehaze）。首先，设计共享先验估计器（SPE），通过迭代训练确保光照与反照率的一致性，生成高质量清晰图像；引入自监控机制以消除不良特征，提供可靠先验用于图像重建。其次，提出动态小波可分卷积（DWSC），有效整合低频与高频关键特征，显著提升细节保留能力并保证全局一致性。最后，设计自适应颜色校正模块，解决颜色模糊问题。在基准数据集上的PSNR、SSIM、LPIPS、FID和CIEDE2000指标均达到领先水平，且有助于下游任务性能提升。项目代码将开源于https://github.com/Fan-pixel/UR2P-Dehaze。
原文摘要 · Abstract (English)
Image dehazing techniques aim to enhance contrast and restore details, which are essential for preserving visual information and improving image processing accuracy. Existing methods rely on a single manual prior, which cannot effectively reveal image details. To overcome this limitation, we propose an unpaired image dehazing network, called the Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior (UR2P-Dehaze). First, to accurately estimate the illumination, reflectance, and color information of the hazy image, we design a shared prior estimator (SPE) that is iteratively trained to ensure the consistency of illumination and reflectance, generating clear, high-quality images. Additionally, a self-monitoring mechanism is introduced to eliminate undesirable features, providing reliable priors for image reconstruction. Next, we propose Dynamic Wavelet Separable Convolution (DWSC), which effectively integrates key features across both low and high frequencies, significantly enhancing the preservation of image details and ensuring global consistency. Finally, to effectively restore the color information of the image, we propose an Adaptive Color Corrector that addresses the problem of unclear colors. The PSNR, SSIM, LPIPS, FID and CIEDE2000 metrics on the benchmark dataset show that our method achieves state-of-the-art performance. It also contributes to the performance improvement of downstream tasks. The project code will be available at https://github.com/Fan-pixel/UR2P-Dehaze. \end{abstract}
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