Under Display Cameras present a promising opportunity for phone manufacturers to achieve bezel-free displays by positioning the camera behind semi-transparent OLED screens. Unfortunately, such imaging systems suffer from severe image degradation due to light attenuation and diffraction effects. In this work, we present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems. A Low-Resolution Network first restores image quality at low-resolution, which is subsequently used by the Guided Filter Network as a filtering input to produce a high-resolution output. Besides the initial downsampling, our low-resolution network uses multiple, parallel atrous convolutions to preserve spatial resolution and emulates multi-scale processing. Our approach's ability to directly train on megapixel images results in significant performance improvement. We additionally propose a simple simulation scheme to pre-train our model and boost performance. Our overall framework ranks 2nd and 5th in the RLQ-TOD'20 UDC Challenge for POLED and TOLED displays, respectively.
@article{arxiv.2008.06229,
title = {Deep Atrous Guided Filter for Image Restoration in Under Display Cameras},
author = {Varun Sundar and Sumanth Hegde and Divya Kothandaraman and Kaushik Mitra},
journal= {arXiv preprint arXiv:2008.06229},
year = {2020}
}
Comments
To appear in ECCV 2020 RLQ Workshop. Supplementary material attached. For project website, see https://varun19299.github.io/deep-atrous-guided-filter/