# UCMNet: Uncertainty-Aware Context Memory Network for Under-Display Camera Image Restoration

arXiv: https://arxiv.org/abs/2604.00381 · HTML: https://mlsanity.com/paper/2604.00381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Data Uncertainty Learning in Face Recognition](https://mlsanity.com/paper/2003.11339/) (2020)
- [Global Structure-Aware Diffusion Process for Low-Light Image Enhancement](https://mlsanity.com/paper/2310.17577/) (2023)
- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)
- [Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model](https://mlsanity.com/paper/2311.11638/) (2023)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [DarkIR: Robust Low-Light Image Restoration](https://mlsanity.com/paper/2412.13443/) (2024)
- [Image Restoration for Under-Display Camera](https://mlsanity.com/paper/2003.04857/) (2020)
- [Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks](https://mlsanity.com/paper/2308.14153/) (2023)
- [Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network](https://mlsanity.com/paper/2104.09556/) (2021)
- [Transfer CLIP for Generalizable Image Denoising](https://mlsanity.com/paper/2403.15132/) (2024)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation](https://mlsanity.com/paper/2312.10998/) (2023)
- [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)
- [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)
- [Gyro-based Neural Single Image Deblurring](https://mlsanity.com/paper/2404.00916/) (2024)
- [DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement](https://mlsanity.com/paper/2209.07937/) (2022)

## Abstract (arXiv)

Under-display cameras (UDCs) allow for full-screen designs by positioning the imaging sensor underneath the display. Nonetheless, light diffraction and scattering through the various display layers result in spatially varying and complex degradations, which significantly reduce high-frequency details. Current PSF-based physical modeling techniques and frequency-separation networks are effective at reconstructing low-frequency structures and maintaining overall color consistency. However, they still face challenges in recovering fine details when dealing with complex, spatially varying degradation. To solve this problem, we propose a lightweight Uncertainty-aware Context-Memory Network (UCMNet), for UDC image restoration. Unlike previous methods that apply uniform restoration, UCMNet performs uncertainty-aware adaptive processing to restore high-frequency details in regions with varying degradations. The estimated uncertainty maps, learned through an uncertainty-driven loss, quantify spatial uncertainty induced by diffraction and scattering, and guide the Memory Bank to retrieve region-adaptive context from the Context Bank. This process enables effective modeling of the non-uniform degradation characteristics inherent to UDC imaging. Leveraging this uncertainty as a prior, UCMNet achieves state-of-the-art performance on multiple benchmarks with 30% fewer parameters than previous models. Project page: hrefhttps://kdhrick2222.github.io/projects/UCMNet/https://kdhrick2222.github.io/projects/UCMNet.