  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.
