# MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal

arXiv: https://arxiv.org/abs/2406.18079 · HTML: https://mlsanity.com/paper/2406.18079/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [On Efficient Transformer-Based Image Pre-training for Low-Level Vision](https://mlsanity.com/paper/2112.10175/) (2021)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [How to Train Neural Networks for Flare Removal](https://mlsanity.com/paper/2011.12485/) (2020)
- [Flare7K: A Phenomenological Nighttime Flare Removal Dataset](https://mlsanity.com/paper/2210.06570/) (2022)
- [Nighttime Dehazing with a Synthetic Benchmark](https://mlsanity.com/paper/2008.03864/) (2020)
- [High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net](https://mlsanity.com/paper/2308.14221/) (2023)
- [A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement](https://mlsanity.com/paper/2301.08880/) (2023)
- [Automatic Flare Spot Artifact Detection and Removal in Photographs](https://mlsanity.com/paper/2103.04384/) (2021)
- [Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring](https://mlsanity.com/paper/2308.05543/) (2023)

## Built on by (most cited first, 16 of 16)

- [Reversible Decoupling Network for Single Image Reflection Removal](https://mlsanity.com/paper/2410.08063/) (2024)
- [Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2407.10649/) (2024)
- [SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2409.00346/) (2024)
- [Underwater Image Restoration via Polymorphic Large Kernel CNNs](https://mlsanity.com/paper/2412.18459/) (2024)
- [Test-Time Intensity Consistency Adaptation for Shadow Detection](https://mlsanity.com/paper/2410.07695/) (2024)
- [LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal](https://mlsanity.com/paper/2510.15868/) (2025)
- [FlareX: A Physics-Informed Dataset for Lens Flare Removal via 2D Synthesis and 3D Rendering](https://mlsanity.com/paper/2510.09995/) (2025)
- [UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2408.10653/) (2024)
- [DFDNet: Dynamic Frequency-Guided De-Flare Network](https://mlsanity.com/paper/2507.17489/) (2025)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [SLCFormer: Spectral-Local Context Transformer with Physics-Grounded Flare Synthesis for Nighttime Flare Removal](https://mlsanity.com/paper/2512.15221/) (2025)
- [Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares](https://mlsanity.com/paper/2605.18156/) (2026)
- [LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography](https://mlsanity.com/paper/2606.06901/) (2026)
- [Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks](https://mlsanity.com/paper/2606.14504/) (2026)
- [DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Data](https://mlsanity.com/paper/2606.27576/) (2026)
- [Benchmarking RAW and RGB Restoration in Image Signal Processors](https://mlsanity.com/paper/2609.02831/) (2026)

## Abstract (arXiv)

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.