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Deep learning based methods have achieved significant success in the task ofsingle image reflection removal (SIRR). However, the majority of these methodsare focused on High-Definition/Standard-Definition (HD/SD) images, whileignoring higher resolution images such as Ultra-High-Definition (UHD) images.With the increasing prevalence of UHD images captured by modern devices, inthis paper, we aim to address the problem of UHD SIRR. Specifically, we firstsynthesize two large-scale UHD datasets, UHDRR4K and UHDRR8K. The UHDRR4Kdataset consists of $2,999$ and $168$ quadruplets of images for training andtesting respectively, and the UHDRR8K dataset contains $1,014$ and $105$quadruplets. To the best of our knowledge, these two datasets are the firstlargest-scale UHD datasets for SIRR. Then, we conduct a comprehensiveevaluation of six state-of-the-art SIRR methods using the proposed datasets.Based on the results, we provide detailed discussions regarding the strengthsand limitations of these methods when applied to UHD images. Finally, wepresent 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 FeatureExtraction Module. These modules extract hypercolumn features, global andpartial attention features, and multi-scale spatial features, respectively. Toensure effective training, we utilize three terms in our loss function: pixelloss, feature loss, and adversarial loss. We demonstrate through experimentalresults that RRFormer achieves state-of-the-art performance on both the non-UHDdataset and our proposed UHDRR datasets. The code and datasets are publiclyavailable athttps://github.com/Liar-zzy/Benchmarking-Ultra-High-Definition-Single-Image-Reflection-Removal.

Benchmarking Ultra-High-Definition Image Reflection Removal