# Location-aware Single Image Reflection Removal

arXiv: https://arxiv.org/abs/2012.07131 · HTML: https://mlsanity.com/paper/2012.07131/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 30 of 43)

- [Single Image Reflection Separation via Component Synergy](https://mlsanity.com/paper/2308.10027/) (2023)
- [Revisiting Single Image Reflection Removal In the Wild](https://mlsanity.com/paper/2311.17320/) (2023)
- [Reversible Decoupling Network for Single Image Reflection Removal](https://mlsanity.com/paper/2410.08063/) (2024)
- [Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections](https://mlsanity.com/paper/2304.08706/) (2023)
- [Language-guided Image Reflection Separation](https://mlsanity.com/paper/2402.11874/) (2024)
- [Robust Reflection Removal with Flash-only Cues in the Wild](https://mlsanity.com/paper/2211.02914/) (2022)
- [PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs](https://mlsanity.com/paper/2402.08657/) (2024)
- [Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision](https://mlsanity.com/paper/2504.04903/) (2025)
- [Dereflection Any Image with Diffusion Priors and Diversified Data](https://mlsanity.com/paper/2503.17347/) (2025)
- [Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation](https://mlsanity.com/paper/2501.00637/) (2024)
- [Indoor Obstacle Discovery on Reflective Ground via Monocular Camera](https://mlsanity.com/paper/2401.01445/) (2024)
- [A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal](https://mlsanity.com/paper/2503.01938/) (2025)
- [Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach](https://mlsanity.com/paper/2309.02429/) (2023)
- [UniSER: A Foundation Model for Unified Soft Effects Removal](https://mlsanity.com/paper/2511.14183/) (2025)
- [Depth-Synergized Mamba Meets Memory Experts for All-Day Image Reflection Separation](https://mlsanity.com/paper/2601.00322/) (2026)
- [3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization](https://mlsanity.com/paper/2404.02634/) (2024)
- [FIRM: Flexible Interactive Reflection reMoval](https://mlsanity.com/paper/2406.01555/) (2024)
- [Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal](https://mlsanity.com/paper/2502.10714/) (2025)
- [Rectifying Latent Space for Generative Single-Image Reflection Removal](https://mlsanity.com/paper/2512.06358/) (2025)
- [Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective](https://mlsanity.com/paper/2204.01505/) (2022)
- [Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats](https://mlsanity.com/paper/2410.02764/) (2024)
- [F2T2-HiT: A U-Shaped FFT Transformer and Hierarchical Transformer for Reflection Removal](https://mlsanity.com/paper/2506.05489/) (2025)
- [GFRRN: Explore the Gaps in Single Image Reflection Removal](https://mlsanity.com/paper/2602.22695/) (2026)
- [ViM: Vision Middleware for Unified Downstream Transferring](https://mlsanity.com/paper/2303.06911/) (2023)
- [Utilizing Multi-step Loss for Single Image Reflection Removal](https://mlsanity.com/paper/2412.08582/) (2024)
- [Single Image Reflection Separation via Dual Prior Interaction Transformer](https://mlsanity.com/paper/2505.12641/) (2025)
- [OpenRR-5k: A Large-Scale Benchmark for Reflection Removal in the Wild](https://mlsanity.com/paper/2506.05482/) (2025)
- [ReflexSplit: Single Image Reflection Separation via Layer Fusion-Separation](https://mlsanity.com/paper/2601.17468/) (2026)
- [FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal](https://mlsanity.com/paper/2603.19036/) (2026)
- [EvReflection: Event-Driven Micro-Dynamics for Reflection Removal](https://mlsanity.com/paper/2608.06184/) (2026)

## Abstract (arXiv)

This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or transmission-dominated, and it is used as a cue for the network to control the feature flow when predicting the reflection and transmission layers. We design our network as a recurrent network to progressively refine reflection removal results at each iteration. The novelty is that we leverage Laplacian kernel parameters to emphasize the boundaries of strong reflections. It is beneficial to strong reflection detection and substantially improves the quality of reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches. Our code and the pre-trained model can be found at https://github.com/zdlarr/Location-aware-SIRR.