# Single Image Reflection Separation via Component Synergy

arXiv: https://arxiv.org/abs/2308.10027 · HTML: https://mlsanity.com/paper/2308.10027/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Hypercolumns for Object Segmentation and Fine-grained Localization](https://mlsanity.com/paper/1411.5752/) (2014)
- [Single Image Reflection Separation with Perceptual Losses](https://mlsanity.com/paper/1806.05376/) (2018)
- [Polarized Reflection Removal with Perfect Alignment in the Wild](https://mlsanity.com/paper/2003.12789/) (2020)
- [A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing](https://mlsanity.com/paper/1708.03474/) (2017)
- [Single Image Reflection Removal through Cascaded Refinement](https://mlsanity.com/paper/1911.06634/) (2019)
- [Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements](https://mlsanity.com/paper/1904.00637/) (2019)
- [Location-aware Single Image Reflection Removal](https://mlsanity.com/paper/2012.07131/) (2020)
- [Learning to See Through Obstructions](https://mlsanity.com/paper/2004.01180/) (2020)
- [Robust Reflection Removal with Reflection-free Flash-only Cues](https://mlsanity.com/paper/2103.04273/) (2021)
- [Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation](https://mlsanity.com/paper/2110.10546/) (2021)
- [CRRN: Multi-Scale Guided Concurrent Reflection Removal Network](https://mlsanity.com/paper/1805.11802/) (2018)
- [Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance](https://mlsanity.com/paper/2012.00945/) (2020)
- [Learning to See Through Obstructions with Layered Decomposition](https://mlsanity.com/paper/2008.04902/) (2020)
- [Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images](https://mlsanity.com/paper/2010.04324/) (2020)
- [Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection](https://mlsanity.com/paper/1605.01825/) (2016)

## Built on by (most cited first, 30 of 38)

- [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)
- [Layered Image Vectorization via Semantic Simplification](https://mlsanity.com/paper/2406.05404/) (2024)
- [PolarFree: Polarization-based Reflection-free Imaging](https://mlsanity.com/paper/2503.18055/) (2025)
- [Neural Spline Fields for Burst Image Fusion and Layer Separation](https://mlsanity.com/paper/2312.14235/) (2023)
- [Language-guided Image Reflection Separation](https://mlsanity.com/paper/2402.11874/) (2024)
- [DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos](https://mlsanity.com/paper/2405.17705/) (2024)
- [PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal](https://mlsanity.com/paper/2402.02374/) (2024)
- [Dereflection Any Image with Diffusion Priors and Diversified Data](https://mlsanity.com/paper/2503.17347/) (2025)
- [Removing Reflections from RAW Photos](https://mlsanity.com/paper/2404.14414/) (2024)
- [Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation](https://mlsanity.com/paper/2501.00637/) (2024)
- [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)
- [FIRM: Flexible Interactive Reflection reMoval](https://mlsanity.com/paper/2406.01555/) (2024)
- [OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal](https://mlsanity.com/paper/2506.08299/) (2025)
- [Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering](https://mlsanity.com/paper/2507.06103/) (2025)
- [Reflection Removal through Efficient Adaptation of Diffusion Transformers](https://mlsanity.com/paper/2512.05000/) (2025)
- [Rectifying Latent Space for Generative Single-Image Reflection Removal](https://mlsanity.com/paper/2512.06358/) (2025)
- [Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats](https://mlsanity.com/paper/2410.02764/) (2024)
- [Degradation-Aware Image Enhancement via Vision-Language Classification](https://mlsanity.com/paper/2506.05450/) (2025)
- [Single-image reflection removal via self-supervised diffusion models](https://mlsanity.com/paper/2412.20466/) (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)
- [Single Image Reflection Separation via Dual Prior Interaction Transformer](https://mlsanity.com/paper/2505.12641/) (2025)
- [ReflexSplit: Single Image Reflection Separation via Layer Fusion-Separation](https://mlsanity.com/paper/2601.17468/) (2026)
- [Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline](https://mlsanity.com/paper/2603.16446/) (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)
- [Reflection Removal Using Recurrent Polarization-to-Polarization Network](https://mlsanity.com/paper/2402.18178/) (2024)
- [Exploring Spectral Characteristics for Single Image Reflection Removal](https://mlsanity.com/paper/2509.12627/) (2025)

## Abstract (arXiv)

The reflection superposition phenomenon is complex and widely distributed in the real world, which derives various simplified linear and nonlinear formulations of the problem. In this paper, based on the investigation of the weaknesses of existing models, we propose a more general form of the superposition model by introducing a learnable residue term, which can effectively capture residual information during decomposition, guiding the separated layers to be complete. In order to fully capitalize on its advantages, we further design the network structure elaborately, including a novel dual-stream interaction mechanism and a powerful decomposition network with a semantic pyramid encoder. Extensive experiments and ablation studies are conducted to verify our superiority over state-of-the-art approaches on multiple real-world benchmark datasets. Our code is publicly available at https://github.com/mingcv/DSRNet.