Reflection Removal

Reflection removal aims to computationally separate reflected light from images, revealing the underlying scene. Current research focuses on improving accuracy and robustness using various deep learning architectures, including transformers, generative adversarial networks (GANs), and recurrent networks, often incorporating additional cues like flash photography, polarization information, or user interaction to address inherent ambiguities. These advancements are significant for improving image quality in consumer photography and various scientific imaging applications, particularly where reflections hinder accurate analysis or interpretation. The development of large-scale, high-quality datasets is also a key area of focus, enabling the training of more effective and generalizable models.