Self-Prior Guided Spatial and Fourier Transformer for Nighttime Flare Removal

When capturing scenes with intense light sources, extensive flare artifacts often obscure the background and degrade image quality. Most flare removal methods directly process the flare-corrupted image as the optimization target, limiting the model’s understanding and generalization in complex real-world scenarios. In this paper, we propose a novel Self-prior Guided Spatial and Fourier Transformer (SGSFT) for nighttime flare removal. Specifically, we first establish a Self-prior Extraction Network to capture inherent priors in different scenes. Subsequently, we introduce a Semantic Contrast Enhancement Strategy to reinforce the semantic irrelevance between flare and light source, enabling the flare removal network to learn pattern differences between them and thus preserve light source. Finally, we build a Spatial and Frequency Flare Removal Network with Spatial Contextual Attention Block (SCAB) and Frequency Global Information Adjustment Block (FGIAB) to generate flare-free image. SCAB can perceive rich contextual information from self-prior guided regions and infer reasonable content. FGIAB captures global luminance representation in the frequency domain to maintain luminance consistency between the inferred regions and the flare-free areas. Extensive experiments demonstrate that the proposed approach achieves optimal performance in real nighttime scenes and exhibits robust generalization across various flare scenarios captured by different electronic devices. Note to Practitioners—The motivation of this paper is to remove flare artifacts in imaging. Flares degrade image quality and impact the performance of advanced vision tasks such as semantic segmentation and depth estimation in autonomous driving. Existing methods that indiscriminately extract contextual information from the entire image limit the model’s understanding of flares. This study proposes a self-prior guided flare removal network. The network first extracts self-prior information from flare-damaged images, then aggregates non-local information from the context indicated by the self-prior information to remove flares and infer semantically plausible fill content. Additionally, we model the global luminance information of the image in the frequency domain to enhance the global luminance consistency of the flare-free image. Experimental results show that our method has strong flare removal capabilities, but it also has a limitation. The training phase of this method requires paired flare-damaged images and flare images, which are difficult to obtain in real-world scenarios. Therefore, we will explore unsupervised flare removal methods in the future.