Contrastive Feature Decomposition for Image Reflection Removal

The crux of image reflection removal stems from the difficulty of recognizing the diverse reflection patterns. Typical methods optimize the modeling of background restoration by performing low-level supervision on the restored image to minimize its per-pixel difference from the groundtruth, which re-lies on substantial training samples to learn diverse reflection patterns robustly and avoid overfitting spurious reflection patterns. In this work, we perform supervision on the contrastive distribution between the predicted background and the reflection image. Specifically, our proposed method restores the background and the reflection images in parallel, and seeks to maximize the distribution consistency between the predicted background-reflection contrast and the groundtruth contrast in the latent space. Such supervision pushes the model to focus on contrastive modeling between the background and reflection image. Extensive experiments on four real-world bench-marks demonstrate that our method consistently outperforms state-of-the-art methods.