# Under-Display Camera Image Restoration with Scattering Effect arXiv: https://arxiv.org/abs/2308.04163 ยท HTML: https://mlsanity.com/paper/2308.04163/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 45 references that are in the map. - First posted on arXiv 8 Aug 2023. ## Builds on (most cited first, 25 of 25) - [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) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021) - [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022) - [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017) - [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019) - [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018) - [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022) - [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021) - [Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2112.13047/) (2021) - [HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization](https://mlsanity.com/paper/2105.13084/) (2021) - [Image Restoration for Under-Display Camera](https://mlsanity.com/paper/2003.04857/) (2020) - [Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network](https://mlsanity.com/paper/2104.09556/) (2021) - [A New Journey from SDRTV to HDRTV](https://mlsanity.com/paper/2108.07978/) (2021) - [Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera](https://mlsanity.com/paper/2304.06019/) (2023) - [MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results](https://mlsanity.com/paper/2209.07052/) (2022) - [Rethinking Performance Gains in Image Dehazing Networks](https://mlsanity.com/paper/2209.11448/) (2022) - [Transform Domain Pyramidal Dilated Convolution Networks For Restoration of Under Display Camera Images](https://mlsanity.com/paper/2009.09393/) (2020) - [UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network](https://mlsanity.com/paper/2209.01809/) (2022) - [Deep Atrous Guided Filter for Image Restoration in Under Display Cameras](https://mlsanity.com/paper/2008.06229/) (2020) - [Modular Degradation Simulation and Restoration for Under-Display Camera](https://mlsanity.com/paper/2209.11455/) (2022) - [Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices](https://mlsanity.com/paper/2211.14040/) (2022) ## Built on by (most cited first, 12 of 12) - [OneRestore: A Universal Restoration Framework for Composite Degradation](https://mlsanity.com/paper/2407.04621/) (2024) - [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024) - [PromptCIR: Blind Compressed Image Restoration with Prompt Learning](https://mlsanity.com/paper/2404.17433/) (2024) - [AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations](https://mlsanity.com/paper/2411.10708/) (2024) - [Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers](https://mlsanity.com/paper/2410.08688/) (2024) - [An Efficient and Mixed Heterogeneous Model for Image Restoration](https://mlsanity.com/paper/2504.10967/) (2025) - [Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur](https://mlsanity.com/paper/2507.00372/) (2025) - [Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2501.18517/) (2025) - [Adversarial Purification and Fine-tuning for Robust UDC Image Restoration](https://mlsanity.com/paper/2402.13629/) (2024) - [Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2403.05906/) (2024) - [AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting](https://mlsanity.com/paper/2504.12605/) (2025) - [UCMNet: Uncertainty-Aware Context Memory Network for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2604.00381/) (2026) ## Abstract (arXiv) The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semi-transparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at urlhttps://github.com/NamecantbeNULL/SRUDC.