# Transform Domain Pyramidal Dilated Convolution Networks For Restoration of Under Display Camera Images

arXiv: https://arxiv.org/abs/2009.09393 · HTML: https://mlsanity.com/paper/2009.09393/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring](https://mlsanity.com/paper/2404.13153/) (2024)
- [MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results](https://mlsanity.com/paper/2209.07052/) (2022)
- [Under-Display Camera Image Restoration with Scattering Effect](https://mlsanity.com/paper/2308.04163/) (2023)
- [Modular Degradation Simulation and Restoration for Under-Display Camera](https://mlsanity.com/paper/2209.11455/) (2022)
- [Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer](https://mlsanity.com/paper/2308.10196/) (2023)
- [Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera](https://mlsanity.com/paper/2403.05660/) (2024)
- [ISP-Agnostic Image Reconstruction for Under-Display Cameras](https://mlsanity.com/paper/2111.01511/) (2021)
- [Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2501.18517/) (2025)
- [Zero-Reference Image Restoration for Under-Display Camera of UAV](https://mlsanity.com/paper/2202.06283/) (2022)
- [Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2403.05906/) (2024)
- [UCMNet: Uncertainty-Aware Context Memory Network for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2604.00381/) (2026)

## Abstract (arXiv)

Under-display camera (UDC) is a novel technology that can make digital imaging experience in handheld devices seamless by providing large screen-to-body ratio. UDC images are severely degraded owing to their positioning under a display screen. This work addresses the restoration of images degraded as a result of UDC imaging. Two different networks are proposed for the restoration of images taken with two types of UDC technologies. The first method uses a pyramidal dilated convolution within a wavelet decomposed convolutional neural network for pentile-organic LED (P-OLED) based display system. The second method employs pyramidal dilated convolution within a discrete cosine transform based dual domain network to restore images taken using a transparent-organic LED (T-OLED) based UDC system. The first method produced very good quality restored images and was the winning entry in European Conference on Computer Vision (ECCV) 2020 challenge on image restoration for Under-display Camera - Track 2 - P-OLED evaluated based on PSNR and SSIM. The second method scored fourth position in Track-1 (T-OLED) of the challenge evaluated based on the same metrics.