论文标题
在显示摄像机下方的图像恢复的深度彻底引导过滤器
Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
论文作者
论文摘要
在显示摄像机下，电话制造商通过将相机放置在半透明的OLED屏幕后面，这是一个有前途的机会，可以实现无边框显示器。不幸的是，由于明亮的衰减和衍射效应，这种成像系统遭受了严重的图像降解。在这项工作中，我们介绍了深度抗过滤器（DAGF），这是UDC系统中图像恢复的两阶段，端到端的方法。低分辨率网络首先在低分辨率下恢复图像质量，随后，引导过滤器网络将其用作过滤输入以产生高分辨率输出。除了最初的倒数采样外，我们的低分辨率网络还使用多个平行的非常弯曲的卷积来保留空间分辨率并模拟多尺度处理。我们的方法直接训练百像型图像的能力会大大提高性能。我们还提出了一个简单的仿真方案，以预先培训我们的模型并提高性能。我们的整体框架分别在RLQ-TOD'20 UDC挑战中排名第2和第五，分别为Poled和poted显示屏排名。
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.
