# Modular Degradation Simulation and Restoration for Under-Display Camera Source: [Modular Degradation Simulation and Restoration for Under-Display Camera]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:36:22.217Z Authors: Yang Zhou, Yuda Song, Xin Du Published: 2022-09-23T07:36:07.000Z ## Abstract Under-display camera (UDC) provides an elegant solution for full-screen smartphones. However, UDC captured images suffer from severe degradation since sensors lie under the display. Although this issue can be tackled by image restoration networks, these networks require large-scale image pairs for training. To this end, we propose a modular network dubbed MPGNet trained using the generative adversarial network (GAN) framework for simulating UDC imaging. Specifically, we note that the UDC imaging degradation process contains brightness attenuation, blurring, and noise corruption. Thus we model each degradation with a characteristic-related modular network, and all modular networks are cascaded to form the generator. Together with a pixel-wise discriminator and supervised loss, we can train the generator to simulate the UDC imaging degradation process. Furthermore, we present a Transformer-style network named DWFormer for UDC image restoration. For practical purposes, we use depth-wise convolution instead of the multi-head self-attention to aggregate local spatial information. Moreover, we propose a novel channel attention module to aggregate global information, which is critical for brightness recovery. We conduct evaluations on the UDC benchmark, and our method surpasses the previous state-of-the-art models by 1.23 dB on the P-OLED track and 0.71 dB on the T-OLED track, respectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [UDC Image Restoration on Synthetic UDC dataset HDRI Haven (test)](): UDC Image Restoration; Synthetic UDC dataset HDRI Haven (test); PSNR: 28.85. ## Original sources and implementation [Original paper]() [PDF]()