When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.

翻译：当对强光源拍摄图像时，所得图像往往包含异质的光晕伪影。这些伪影会严重影响图像视觉质量及下游计算机视觉任务。由于采集用于训练光晕去除模型的真实成对光晕污染/无光晕图像存在困难，现有方法采用直接叠加方式合成数据。然而，这些方法未考虑图像信号处理流程（ISP）中的自动曝光与色调映射，导致基于此类数据训练的深度学习模型泛化能力有限。此外，现有方法因不同光源的尺寸、形状和照度差异，难以处理多光源场景。本文通过重新审视ISP流程并重构合成流程中的自动曝光原理，提出一种更可靠的光源恢复策略，以提升镜头光晕去除性能。新流程采用凸组合区分局部与全局光照，逼近真实成像效果，避免全局光照偏移与局部过饱和现象。我们的多光源恢复策略根据照度水平对神经网络输入输出进行凸平均处理，从而避免在光源识别中采用硬阈值。此外，我们构建了包含十种消费电子产品采集的光晕污染图像的新测试集，以验证光晕去除方法的泛化能力。大量实验表明，本文方案能有效提升镜头光晕去除性能，并将技术前沿推向更通用的场景。