SFNet - A Spatial-Frequency Domain Neural Network for Image Lens Flare Removal

High-intensity light sources in the scene can cause undesired internal reflections between the multiple optical elements of lenses, resulting in loss of contrast and color change. This effect, known as a lens flare, can have artistic value, but it can also limit the performance of downstream tasks. Professional cameras and lenses have complex optical systems with an increased number of elements, designed to control reflections and refractions for optimal light convergence. However, lens flare is still a challenging problem for professional image acquisition, especially due to the limited information published by manufacturers. In this work, we propose an end-to-end deep learning solution for image lens flare removal and a novel dataset, covering popular DSLR/DSLM optical systems. Our model combines information from both the spatial and frequency domains of the image, leveraging the spatial domain local features and the global features in the frequency domain to reconstruct the flare-affected image. Our model achieves state-of-the-art results, outperforming well-established image restoration architectures for image lens flare removal.