Image Lens Flare Removal Using Adversarial Curve Learning

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can significantly 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 training data. However, these methods do not consider automatic exposure and tone mapping in the image signal processing pipeline (ISP), leading to the limited generalization capability of deep model training using such data. Besides, existing light source recovery methods hardly recover 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, remodeling the principle of automatic exposure in the synthesis pipeline, and designing a more reliable light source recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through a convex combination, avoiding global illumination shifting and local over-saturation. Moreover, the current deep models are only generalized to specific devices due to the diversity of cameras' ISPs. To achieve better generalization on different devices, we formulate the generalization problem as an adversarial training problem and embed an adversarial curve learning (ACL) paradigm in the synthesis pipeline to gain better performance. For recovering multiple light sources, our strategy 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 fifteen 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.