DFDNet: Dynamic Frequency-Guided De-Flare Network
Strong light sources in nighttime photography frequently produce flares in images, significantly degrading visual quality and impacting the performance of downstream tasks. While some progress has been made, existing methods continue to struggle with removing large-scale flare artifacts and repairing structural damage in regions near the light source. We observe that these challenging flare artifacts exhibit more significant discrepancies from the reference images in the frequency domain compared to the spatial domain. Therefore, this paper presents a novel dynamic frequency-guided deflare network (DFDNet) that decouples content information from flare artifacts in the frequency domain, effectively removing large-scale flare artifacts. Specifically, DFDNet consists mainly of a global dynamic frequency-domain guidance (GDFG) module and a local detail guidance module (LDGM). The GDFG module guides the network to perceive the frequency characteristics of flare artifacts by dynamically optimizing global frequency domain features, effectively separating flare information from content information. Additionally, we design an LDGM via a contrastive learning strategy that aligns the local features of the light source with the reference image, reduces local detail damage from flare removal, and improves fine-grained image restoration. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of performance. The code is available at \href{https://github.com/AXNing/DFDNet}{https://github.com/AXNing/DFDNet}.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningImage RestorationFlare RemovalSimilar Papers 제목 키워드 기반
Dual Feature Decoupling for Fine-Grained OOD Detection
Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detection methods have been developed under the idealized assumption of l…
Out-of-Distribution DetectionMedical Image ClassificationMFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal
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 ar…
Flare RemovalImage RestorationAnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identifie…
Anomaly Detectionobject-detectionObject DetectionSLCFormer: Spectral-Local Context Transformer with Physics-Grounded Flare Synthesis for Nighttime Flare Removal
Lens flare is a common nighttime artifact caused by strong light sources scattering within camera lenses, leading to hazy streaks, halos, and glare that degrade visual quality. However, existing methods usually fail to e…
Flare RemovalBreaking Spatial Uniformity: Prior-Guided Mamba with Radial Serialization for Lens Flare Removal
Lens flares, caused by complex optical aberrations, severely degrade image quality especially in nighttime photography. Although recent restoration methods have made remarkable progress, most still rely on spatially unif…
Long-range modelingFlare Removal