Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
Yu Li, Ming Liu, Yaling Yi, Qince Li, Dongwei Ren, Wangmeng Zuo

TL;DR
This paper introduces RAGNet, a two-stage deep learning model that effectively removes reflections from single images by estimating reflection layers first and then guiding transmission estimation with reflection-aware guidance, outperforming existing methods.
Contribution
The paper proposes a novel two-stage network with reflection-aware guidance for improved single image reflection removal, incorporating a new guidance module and mask loss for better reflection suppression.
Findings
RAGNet outperforms state-of-the-art methods on five datasets.
The reflection-aware guidance effectively exploits estimated reflection information.
The method achieves superior qualitative and quantitative results.
Abstract
Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical application scenarios. For improving reflection removal, cascaded deep models have been usually adopted to estimate the transmission in a progressive manner. However, most existing methods are still limited in exploiting the result in prior stage for guiding transmission estimation. In this paper, we present a novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR). To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate. Reflectionaware guidance (RAG) module is then elaborated for better exploiting the estimated reflection in predicting transmission layer. By incorporating feature maps from the estimated reflection and observation,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Enhancement Techniques · Optical Systems and Laser Technology · Image and Signal Denoising Methods
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Multi-Head Attention · Attention Is All You Need · Linear Layer · Byte Pair Encoding · Adam · Residual Connection · Dense Connections · Linear Warmup With Linear Decay · Weight Decay
