Deep learning based methods have achieved significant success in the task of single image reflection removal (SIRR). However, the majority of these methods are focused on High-Definition/Standard-Definition (HD/SD) images, while ignoring higher resolution images such as Ultra-High-Definition (UHD) images. With the increasing prevalence of UHD images captured by modern devices, in this paper, we aim to address the problem of UHD SIRR. Specifically, we first synthesize two large-scale UHD datasets, UHDRR4K and UHDRR8K. The UHDRR4K dataset consists of $2,999$ and $168$ quadruplets of images for training and testing respectively, and the UHDRR8K dataset contains $1,014$ and $105$ quadruplets. To the best of our knowledge, these two datasets are the first largest-scale UHD datasets for SIRR. Then, we conduct a comprehensive evaluation of six state-of-the-art SIRR methods using the proposed datasets. Based on the results, we provide detailed discussions regarding the strengths and limitations of these methods when applied to UHD images. Finally, we present a transformer-based architecture named RRFormer for reflection removal. RRFormer comprises three modules, namely the Prepossessing Embedding Module, Self-attention Feature Extraction Module, and Multi-scale Spatial Feature Extraction Module. These modules extract hypercolumn features, global and partial attention features, and multi-scale spatial features, respectively. To ensure effective training, we utilize three terms in our loss function: pixel loss, feature loss, and adversarial loss. We demonstrate through experimental results that RRFormer achieves state-of-the-art performance on both the non-UHD dataset and our proposed UHDRR datasets. The code and datasets are publicly available at https://github.com/Liar-zzy/Benchmarking-Ultra-High-Definition-Single-Image-Reflection-Removal.

翻译：基于深度学习的方法在单图像反射去除任务中已取得显著成功。然而，这些方法大多聚焦于高清/标清图像，而忽略了更高分辨率的图像，例如超高清图像。随着现代设备捕获的超高清图像日益普及，本文旨在解决超高清单图像反射去除问题。具体而言，我们首先合成了两个大规模超高清数据集：UHDRR4K和UHDRR8K。UHDRR4K数据集分别包含2,999组和168组四联图像用于训练和测试，UHDRR8K数据集则包含1,014组和105组四联图像。据我们所知，这两个数据集是当前规模最大的超高清单图像反射去除数据集。随后，我们使用所提数据集对六种先进单图像反射去除方法进行了全面评估。基于评估结果，我们详细讨论了这些方法应用于超高清图像时的优势与局限性。最后，我们提出了一种基于Transformer的反射去除架构RRFormer。RRFormer包含三个模块：预处理嵌入模块、自注意力特征提取模块和多尺度空间特征提取模块。这些模块分别用于提取超列特征、全局与局部注意力特征以及多尺度空间特征。为确保有效训练，我们在损失函数中采用了三项损失：像素损失、特征损失和对抗损失。实验结果表明，RRFormer在非超高清数据集及我们提出的UHDRR数据集上均达到了最先进的性能。代码与数据集已公开于https://github.com/Liar-zzy/Benchmarking-Ultra-High-Definition-Single-Image-Reflection-Removal。