Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation

Tianfu Wang, Mingyang Xie, Haoming Cai, Sachin Shah, Christopher A. Metzler; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 5688-5698

Transparent surfaces, such as glass, create complex reflections that obscure images and challenge downstream computer vision applications. We introduce Flash-Split, a robust framework for separating transmitted and reflected light using a single (potentially misaligned) pair of flash/no-flash images. Our core idea is to perform flash-informed reflection separation iteratively in a low-dimensional latent space. Specifically, Flash-Split consists of two stages. Stage 1 separates the reflection latent and transmission latent via a dual-branch diffusion model that is conditioned on an encoded flash/no-flash latent pair. This stage effectively mitigates the flash/no-flash misalignment issue. Stage 2 restores high-resolution, faithful details to the separated latents via a cross-latent decoding process that is conditioned on the original images before separation. We validate Flash-Split on challenging real-world scenes and demonstrate it significantly outperforms existing methods. Our project webpage is at https://flash-split.github.io/.