Image reflection removal is crucial for restoring image quality. Distorted images can negatively impact tasks like object detection and image segmentation. In this paper, we present a novel approach for image reflection removal using a single image. Instead of focusing on model architecture, we introduce a new training technique that can be generalized to image-to-image problems, with input and output being similar in nature. This technique is embodied in our multi-step loss mechanism, which has proven effective in the reflection removal task. Additionally, we address the scarcity of reflection removal training data by synthesizing a high-quality, non-linear synthetic dataset called RefGAN using Pix2Pix GAN. This dataset significantly enhances the model's ability to learn better patterns for reflection removal. We also utilize a ranged depth map, extracted from the depth estimation of the ambient image, as an auxiliary feature, leveraging its property of lacking depth estimations for reflections. Our approach demonstrates superior performance on the SIR^2 benchmark and other real-world datasets, proving its effectiveness by outperforming other state-of-the-art models.

翻译：图像反射去除对于恢复图像质量至关重要。失真的图像会对物体检测和图像分割等任务产生负面影响。本文提出了一种利用单幅图像进行反射去除的新方法。与聚焦于模型架构不同，我们引入了一种可推广到图像到图像问题的新训练技术，其输入和输出在本质上相似。该技术体现在我们提出的多步损失机制中，该机制在反射去除任务中已被证明有效。此外，我们通过使用Pix2Pix GAN合成一个高质量的非线性合成数据集RefGAN，解决了反射去除训练数据稀缺的问题。该数据集显著增强了模型学习更优反射去除模式的能力。我们还利用从环境图像深度估计中提取的带范围深度图作为辅助特征，利用了反射区域缺乏深度估计的特性。我们的方法在SIR^2基准测试和其他真实世界数据集上表现出优越性能，通过超越其他最先进模型证明了其有效性。