When light is scattered or reflected accidentally in the lens, flare artifacts may appear in the captured photos, affecting the photos' visual quality. The main challenge in flare removal is to eliminate various flare artifacts while preserving the original content of the image. To address this challenge, we propose a lightweight Multi-Frequency Deflare Network (MFDNet) based on the Laplacian Pyramid. Our network decomposes the flare-corrupted image into low and high-frequency bands, effectively separating the illumination and content information in the image. The low-frequency part typically contains illumination information, while the high-frequency part contains detailed content information. So our MFDNet consists of two main modules: the Low-Frequency Flare Perception Module (LFFPM) to remove flare in the low-frequency part and the Hierarchical Fusion Reconstruction Module (HFRM) to reconstruct the flare-free image. Specifically, to perceive flare from a global perspective while retaining detailed information for image restoration, LFFPM utilizes Transformer to extract global information while utilizing a convolutional neural network to capture detailed local features. Then HFRM gradually fuses the outputs of LFFPM with the high-frequency component of the image through feature aggregation. Moreover, our MFDNet can reduce the computational cost by processing in multiple frequency bands instead of directly removing the flare on the input image. Experimental results demonstrate that our approach outperforms state-of-the-art methods in removing nighttime flare on real-world and synthetic images from the Flare7K dataset. Furthermore, the computational complexity of our model is remarkably low.

翻译：当光线在镜头中意外散射或反射时，拍摄的照片中可能出现耀斑伪影，影响照片的视觉质量。耀斑去除的主要挑战在于消除各种耀斑伪影的同时保留图像的原始内容。为解决这一挑战，我们提出了一种基于拉普拉斯金字塔的轻量级多频带去耀斑网络（MFDNet）。我们的网络将受耀斑污染的图像分解为低频和高频带，有效分离图像中的光照信息和内容信息。低频部分通常包含光照信息，而高频部分则包含细节内容信息。因此，我们的MFDNet主要由两个模块构成：用于去除低频部分耀斑的低频耀斑感知模块（LFFPM），以及用于重建无耀斑图像的分层融合重建模块（HFRM）。具体而言，为了从全局视角感知耀斑并保留图像修复所需的细节信息，LFFPM利用Transformer提取全局信息，同时利用卷积神经网络捕捉局部细节特征。随后，HFRM通过特征聚合将LFFPM的输出与图像的高频分量逐步融合。此外，我们的MFDNet通过在多频带进行处理而非直接在输入图像上移除耀斑，能够有效降低计算成本。实验结果表明，在Flare7K数据集的真实场景和合成图像上，我们的方法在去除夜间耀斑方面优于现有先进技术。同时，我们模型的计算复杂度显著较低。