Deep Unsupervised Reflection Removal Using Diffusion Models

Reflections caused due to surfaces such as glass affect the aesthetics of an image, and are hence undesirable. Most of the recent works on reflection removal use supervised learning based approaches using deep neural networks. However, most of these methods require large amount of paired data for training, which is difficult to obtain. Moreover, it is difficult to deploy existing deep learning based algorithms on multiple devices with different computational power, since it is very hard to control the trade-off between the strength of reflection removal and computational complexity during inference. To address these challenges, we propose a novel deep learning based approach for reflection removal, that is both unsupervised and controllable. We use Denoising Diffusion Probability Models to learn a distribution of reflection-free images. The learnt model is then used to generate reflection-free images using an input conditioned forward diffusion process during inference. We also perform qualitative and quantitative comparison and show that our method is at par or better than existing methods for deep supervised reflection removal, while outperforming unsupervised method by ~ 6.5 dB.