The field of computer vision is witnessing significant advancements in reflection removal and image enhancement, with a focus on developing innovative methods to tackle complex real-world scenarios. Researchers are working on creating large-scale, high-quality datasets to train and evaluate reflection removal models, which is crucial for improving the robustness and accuracy of these models. Novel architectures, such as Transformer-based designs, are being proposed to effectively capture and separate reflection patterns. Furthermore, the development of scalable datasets and benchmark challenges is facilitating the evaluation and comparison of different methods. Noteworthy papers include:

• OpenRR-5k, which introduces a large-scale benchmark for Single Image Reflection Removal, and
• F2T2-HiT, which proposes a U-shaped Fast Fourier Transform Transformer and Hierarchical Transformer architecture for reflection removal. These advancements are expected to have significant applications in photography, image enhancement, and other computer vision tasks.