arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.06229cs.CV
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基于深度Atrous导引滤波器的欠显相机图像复原

Deep Atrous Guided Filter for Image Restoration in Under Display Cameras

Indian Institute of Technology Madras(印度理工学院马德拉斯分校)

Varun Sundar, Sumanth Hegde, Divya Kothandaraman, Kaushik Mitra

摘要:

Under Display相机为手机制造商提供了一个充满希望的机会,通过将相机定位在半透明OLED屏幕后面来实现无边框显示。不幸的是,这种成像系统由于光衰减和衍射效应而遭受严重的图像退化。在这项工作中,我们提出了深度Atrous引导滤波器(DAGF),一个两阶段,端到端的方法,图像复原在UDC系统。低分辨率网络首先以低分辨率恢复图像质量,随后被引导滤波器网络用作滤波输入以产生高分辨率输出。除了初始下采样,我们的低分辨率网络还使用多个并行的无环卷积来保持空间分辨率,并模拟多尺度处理。我们的方法的能力,直接训练的百万像素图像的结果在显着的性能提高。我们还提出了一个简单的模拟方案来预训练我们的模型并提高性能。我们的整体框架在RLQ-TOD'20 UDC挑战赛中分别在POLED和TOLED显示屏领域排名第二和第五。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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