Abstract
Removing reflections is a crucial task in computer vision, with significant applications in photography and image enhancement. Nevertheless, existing methods are constrained by the absence of large-scale, high-quality, and diverse datasets. In this paper, we present a novel benchmark for Single Image Reflection Removal (SIRR). We have developed a large-scale dataset containing 5,300 high-quality, pixel-aligned image pairs, each consisting of a reflection image and its corresponding clean version. Specifically, the dataset is divided into two parts: 5,000 images are used for training, and 300 images are used for validation. Additionally, we have included 100 real-world testing images without ground truth (GT) to further evaluate the practical performance of reflection removal methods. All image pairs are precisely aligned at the pixel level to guarantee accurate supervision. The dataset encompasses a broad spectrum of real-world scenarios, featuring various lighting conditions, object types, and reflection patterns, and is segmented into training, validation, and test sets to facilitate thorough evaluation. To validate the usefulness of our dataset, we train a U-Net-based model and evaluate it using five widely-used metrics, including PSNR, SSIM, LPIPS, DISTS, and NIQE. We will release both the dataset and the code on this https URL to facilitate future research in this field.
Abstract (translated)
移除图像中的反射是计算机视觉领域的一项重要任务，在摄影和图像增强方面有着广泛的应用。然而，现有的方法受到大规模、高质量且多样化的数据集缺乏的限制。在本文中，我们提出了一套用于单张图片去反射(Single Image Reflection Removal, SIRR)的新基准测试。为此，我们开发了一个包含5,300对高分辨率像素级对齐图像的数据集，每一对包括带有反射的原始图和对应的无反射干净图。 具体来说，该数据集被分为两部分：其中5,000张用于训练，另外300张用于验证。此外，我们还包含了一组100张实际场景下的测试图片（没有真实标签），以便进一步评估去反射方法的实用性能。所有的图像对都经过像素级精确对齐以保证监督信号的准确性。 该数据集涵盖了各种现实世界中的场景，包括多样的光照条件、物体类型和反射模式，并且按照训练、验证和测试三个部分进行划分，以便全面评估。 为了展示我们数据集的有效性，我们在U-Net基础上训练了一个模型，并使用五个常用的评价指标（PSNR、SSIM、LPIPS、DISTS、NIQE）进行了评测。我们将在这个网址发布该数据集及代码，以促进未来在该领域的研究工作。
URL
https://arxiv.org/abs/2506.05482
