# MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results arXiv: https://arxiv.org/abs/2209.07052 ยท HTML: https://mlsanity.com/paper/2209.07052/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 41 references that are in the map. - First posted on arXiv 15 Sep 2022. ## Builds on (most cited first, 30 of 35) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [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) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [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) - [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019) - [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018) - [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021) - [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018) - [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022) - [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021) - [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018) - [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021) - [Lightweight Image Super-Resolution with Information Multi-distillation Network](https://mlsanity.com/paper/1909.11856/) (2019) - [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016) - [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018) - [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022) - [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020) - [Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging](https://mlsanity.com/paper/2103.07152/) (2021) - [HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization](https://mlsanity.com/paper/2105.13084/) (2021) - [Seven ways to improve example-based single image super resolution](https://mlsanity.com/paper/1511.02228/) (2015) - [How to Train Neural Networks for Flare Removal](https://mlsanity.com/paper/2011.12485/) (2020) - [NTIRE 2021 Challenge on Image Deblurring](https://mlsanity.com/paper/2104.14854/) (2021) - [Image Restoration for Under-Display Camera](https://mlsanity.com/paper/2003.04857/) (2020) - [Basis Prediction Networks for Effective Burst Denoising with Large Kernels](https://mlsanity.com/paper/1912.04421/) (2019) ## Built on by (most cited first, 14 of 14) - [Perceptual Image Enhancement for Smartphone Real-Time Applications](https://mlsanity.com/paper/2210.13552/) (2022) - [Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera](https://mlsanity.com/paper/2304.06019/) (2023) - [Under-Display Camera Image Restoration with Scattering Effect](https://mlsanity.com/paper/2308.04163/) (2023) - [MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results](https://mlsanity.com/paper/2305.13770/) (2023) - [Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices](https://mlsanity.com/paper/2211.14040/) (2022) - [MIPI 2023 Challenge on RGBW Remosaic: Methods and Results](https://mlsanity.com/paper/2304.10551/) (2023) - [Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer](https://mlsanity.com/paper/2308.10196/) (2023) - [Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark](https://mlsanity.com/paper/2211.00859/) (2022) - [MIPI 2023 Challenge on RGBW Fusion: Methods and Results](https://mlsanity.com/paper/2304.10089/) (2023) - [Deep Video Restoration for Under-Display Camera](https://mlsanity.com/paper/2309.04752/) (2023) - [UDC-VIT: A Real-World Video Dataset for Under-Display Cameras](https://mlsanity.com/paper/2501.18545/) (2025) - [NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2510.13670/) (2025) - [Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising](https://mlsanity.com/paper/2210.00545/) (2022) - [Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2403.05906/) (2024) ## Abstract (arXiv) Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.