  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.
