Lens flare is a common phenomenon when strong light rays arrive at the camera sensor and a clean scene is consequently mixed up with various opaque and semi-transparent artifacts. Existing deep learning methods are always constrained with limited real image pairs for training. Though recent synthesis-based approaches are found effective, synthesized pairs still deviate from the real ones as the mixing mechanism of flare artifacts and scenes in the wild always depends on a line of undetermined factors, such as lens structure, scratches, etc. In this paper, we present a new perspective from the blind nature of the flare removal task in a knowledge-driven manner. Specifically, we present a simple yet effective flare-level estimator to predict the corruption level of a flare-corrupted image. The estimated flare-level can be interpreted as additive information of the gap between corrupted images and their flare-free correspondences to facilitate a network at both training and testing stages adaptively. Besides, we utilize a flare-level modulator to better integrate the estimations into networks. We also devise a flare-aware block for more accurate flare recognition and reconstruction. Additionally, we collect a new real-world flare dataset for benchmarking, namely WiderFlare. Extensive experiments on three benchmark datasets demonstrate that our method outperforms state-of-the-art methods quantitatively and qualitatively.

中文翻译：

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使用知识驱动的 Flare-Level Estimator 进行盲目 Flare Removal


当强光线到达相机传感器时，镜头耀斑是一种常见现象，因此干净的场景与各种不透明和半透明的伪像混合在一起。现有的深度学习方法总是受到有限的真实图像对的限制，用于训练。尽管最近发现基于合成的方法有效，但合成对仍然与真实对不同，因为耀斑伪影和野外场景的混合机制始终取决于一系列不确定的因素，例如镜头结构、划痕等。在本文中，我们以知识驱动的方式从耀斑清除任务的盲目性质提出了一个新的视角。具体来说，我们提出了一个简单而有效的眩光级估计器来预测眩光损坏图像的损坏级别。估计的耀斑水平可以解释为损坏图像与其无耀斑对应关系之间差距的加法信息，以自适应地促进网络在训练和测试阶段。此外，我们利用 Flare 级调制器更好地将估计集成到网络中。我们还设计了一个耀斑感知块，以实现更准确的耀斑识别和重建。此外，我们还收集了一个新的真实世界耀斑数据集进行基准测试，即 WiderFlare。对三个基准数据集的广泛实验表明，我们的方法在定量和定性方面都优于最先进的方法。