# Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection

arXiv: https://arxiv.org/abs/1605.01825 · HTML: https://mlsanity.com/paper/1605.01825/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 10 of 10)

- [Single Image Reflection Separation via Component Synergy](https://mlsanity.com/paper/2308.10027/) (2023)
- [Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation](https://mlsanity.com/paper/2110.10546/) (2021)
- [Reversible Decoupling Network for Single Image Reflection Removal](https://mlsanity.com/paper/2410.08063/) (2024)
- [NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields](https://mlsanity.com/paper/2309.13039/) (2023)
- [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)
- [Single Image Reflection Separation via Dual Prior Interaction Transformer](https://mlsanity.com/paper/2505.12641/) (2025)
- [FUMO: Prior-Modulated Diffusion for Single Image Reflection Removal](https://mlsanity.com/paper/2603.19036/) (2026)
- [Reflection Removal Using Recurrent Polarization-to-Polarization Network](https://mlsanity.com/paper/2402.18178/) (2024)
- [Principled Reflection Separation via Nonlinear Superposition and Feature Interaction](https://mlsanity.com/paper/2606.02831/) (2026)

## Abstract (arXiv)

This paper deals with a challenging, frequently encountered, yet not properly investigated problem in two-frame optical flow estimation. That is, the input frames are compounds of two imaging layers -- one desired background layer of the scene, and one distracting, possibly moving layer due to transparency or reflection. In this situation, the conventional brightness constancy constraint -- the cornerstone of most existing optical flow methods -- will no longer be valid. In this paper, we propose a robust solution to this problem. The proposed method performs both optical flow estimation, and image layer separation. It exploits a generalized double-layer brightness consistency constraint connecting these two tasks, and utilizes the priors for both of them. Experiments on both synthetic data and real images have confirmed the efficacy of the proposed method. To the best of our knowledge, this is the first attempt towards handling generic optical flow fields of two-frame images containing transparency or reflection.