Personalized Single Image Reflection Removal Network through Adaptive Cascade Refinement

In this paper, we aim to restore a reflection-free image from a single reflection-contaminated image captured through the glass. Many deep-learning-based methods attempt to solve the challenging problem by utilizing a uniform model obtained from training data for all test images. Hence, the distinctive characteristics of the test images are not considered. Besides, several methods use a cascade structure in image restoration to refine the results. But they blindly cascade modules with the same weights, improving the model's performance only to a certain extent. To address these problems, we propose a personalized single-image reflection removal network through adaptive cascade refinement (PNACR) based on meta-learning and self-supervised learning. While meta-learning can rapidly adapt to a new task with a few samples, PNACR can remove reflections of a new image with its distinctive characteristics learned by self-supervised learning. Furthermore, the proposed adaptive cascade model can adjust the weights of the model at the next iteration according to the output of the model at the current iteration, significantly improving the model's performance. Hence, the proposed model can learn information from both external training data and the new input image to provide a personalized reflection removal model for each new input image. Extensive comparison and ablation experiments on publicly available datasets demonstrate the validity of the proposed method in quantitative evaluation metrics and qualitative visualization.