We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. This flash-only image is visually reflection-free and thus can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23dB in PSNR. We extend our approach to handheld photography to address the misalignment between the flash and no-flash pair. With misaligned training data and the alignment module, our aligned model outperforms our previous version by more than 3.19dB in PSNR on a misaligned dataset. We also study using linear RGB images as training data. Our source code and dataset are publicly available at https://github.com/ChenyangLEI/flash-reflection-removal.

翻译：我们提出一种简单而有效的无反射线索，用于从一对闪光和环境（无闪光）图像中稳健地消除反射。该无反射线索利用在原始数据空间中从对应闪光图像中减去环境图像得到的仅闪光图像。仅闪光图像等效于在黑暗环境中仅开启闪光灯拍摄的图像。该图像视觉上无反射，因此可为推断环境图像中的反射提供稳健线索。由于仅闪光图像通常存在伪影，我们进一步提出专用模型，既能利用无反射线索，又能避免引入伪影，从而精确估计反射和透射分量。在包含多种反射类型的真实世界图像上的实验表明，我们的模型凭借无反射的仅闪光线索具有显著效果：在PSNR指标上，我们的模型以超过5.23dB的优势优于现有最优反射消除方法。我们将该方法扩展至手持摄影场景，以解决闪光与无闪光图像对之间的未对齐问题。通过使用未对齐训练数据和对齐模块，我们的对齐模型在未对齐数据集上的PSNR比先前版本提升超过3.19dB。我们还研究了使用线性RGB图像作为训练数据的情况。源代码和数据集已公开在https://github.com/ChenyangLEI/flash-reflection-removal。