SMFR-Net: simple multi-domain flare removal network.

When strong light enters the lens, multiple internal reflections and scattering can cause flare, significantly degrading image quality and affecting the performance of downstream vision tasks. In practical photography, flare is often caused by multiple strong light sources, resulting in artifacts such as bright streaks or diffuse halos, which often cover large areas of the image. To effectively remove flare, the network needs to have a large receptive field. However, although the native Transformer architecture has global modeling capability, its computational complexity grows with the square of the image resolution, making it difficult to apply on resource-constrained devices. The windowed attention mechanism, as a compromise, improves computational efficiency but limits the receptive field to within the window, making it difficult to achieve true global perception. To address these issues, we propose a simple multi-domain image flare removal network-SMFR-Net, which achieves state-of-the-art (SOTA) performance with 7.981M parameters. Specifically, SMFR-Net consists of an encoder that jointly models the frequency and spatial domains, and a decoder with a simplified structure. The encoder first enhances global contextual awareness using a frequency domain module with Fourier Transform, then further expands the receptive field through a spatial domain module combined with multi-scale dilated convolutions, and introduces a Channel-Spatial Attention Mechanism to precisely locate the flare regions. The decoder, based on this, discards frequency domain modeling and simplifies the structure to reduce redundant computation. Furthermore, we design a structure-aware composite loss function for the network to improve overall performance. Experimental results show that SMFR-Net outperforms existing methods on the Flare7K++ real-world test set, synthetic test set, and several real-world scenes across most metrics, demonstrating superior flare removal performance and good application potential with its simple and efficient structure.