# Removing Image Artifacts From Scratched Lens Protectors

arXiv: https://arxiv.org/abs/2302.05746 · HTML: https://mlsanity.com/paper/2302.05746/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [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)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [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)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Heterogeneous Domain Generalization via Domain Mixup](https://mlsanity.com/paper/2009.05448/) (2020)
- [Image Inpainting via Conditional Texture and Structure Dual Generation](https://mlsanity.com/paper/2108.09760/) (2021)
- [You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network](https://mlsanity.com/paper/2006.16829/) (2020)
- [Contextual-based Image Inpainting: Infer, Match, and Translate](https://mlsanity.com/paper/1711.08590/) (2017)
- [How to Train Neural Networks for Flare Removal](https://mlsanity.com/paper/2011.12485/) (2020)
- [Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network](https://mlsanity.com/paper/2104.09556/) (2021)
- [Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation](https://mlsanity.com/paper/2107.02629/) (2021)
- [Automatic Flare Spot Artifact Detection and Removal in Photographs](https://mlsanity.com/paper/2103.04384/) (2021)
- [Eye In-Painting with Exemplar Generative Adversarial Networks](https://mlsanity.com/paper/1712.03999/) (2017)
- [Variational Disentanglement for Domain Generalization](https://mlsanity.com/paper/2109.05826/) (2021)
- [Face Image Reflection Removal](https://mlsanity.com/paper/1903.00865/) (2019)
- [Let's See Clearly: Contaminant Artifact Removal for Moving Cameras](https://mlsanity.com/paper/2104.08852/) (2021)

## Built on by (most cited first, 2 of 2)

- [TransLight: Image-Guided Customized Lighting Control with Generative Decoupling](https://mlsanity.com/paper/2508.14814/) (2025)
- [Enhancing Low-Light Images Using Infrared-Encoded Images](https://mlsanity.com/paper/2307.04122/) (2023)

## Abstract (arXiv)

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released after acceptance.