# Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal

arXiv: https://arxiv.org/abs/2502.10714 · HTML: https://mlsanity.com/paper/2502.10714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Single Image Reflection Separation with Perceptual Losses](https://mlsanity.com/paper/1806.05376/) (2018)
- [DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation](https://mlsanity.com/paper/2104.10834/) (2021)
- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [Flare7K: A Phenomenological Nighttime Flare Removal Dataset](https://mlsanity.com/paper/2210.06570/) (2022)
- [Location-aware Single Image Reflection Removal](https://mlsanity.com/paper/2012.07131/) (2020)
- [Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution](https://mlsanity.com/paper/2308.01738/) (2023)
- [Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond](https://mlsanity.com/paper/2306.04236/) (2023)
- [Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior](https://mlsanity.com/paper/2303.15046/) (2023)
- [NightHaze: Nighttime Image Dehazing via Self-Prior Learning](https://mlsanity.com/paper/2403.07408/) (2024)
- [A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint](https://mlsanity.com/paper/2403.18548/) (2024)
- [Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery](https://mlsanity.com/paper/2308.16460/) (2023)
- [Automatic Flare Spot Artifact Detection and Removal in Photographs](https://mlsanity.com/paper/2103.04384/) (2021)
- [Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids](https://mlsanity.com/paper/2111.05700/) (2021)
- [From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement](https://mlsanity.com/paper/2307.16783/) (2023)

## Built on by (most cited first, 4 of 4)

- [UniSER: A Foundation Model for Unified Soft Effects Removal](https://mlsanity.com/paper/2511.14183/) (2025)
- [SLCFormer: Spectral-Local Context Transformer with Physics-Grounded Flare Synthesis for Nighttime Flare Removal](https://mlsanity.com/paper/2512.15221/) (2025)
- [Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods](https://mlsanity.com/paper/2608.28674/) (2026)
- [Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares](https://mlsanity.com/paper/2605.18156/) (2026)

## Abstract (arXiv)

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flare relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks.