论文标题
转换域锥体扩张的卷积网络，用于恢复显示摄像头图像
Transform Domain Pyramidal Dilated Convolution Networks For Restoration of Under Display Camera Images
论文作者
论文摘要
播放摄像头（UDC）是一项新型技术，可以通过提供较大的屏幕与身体比率在无缝的手持设备中获得数字成像体验。由于其在显示屏下的位置，UDC图像被严重降低。这项工作解决了由于UDC成像而导致的恢复图像的恢复。提出了两个不同的网络，以恢复使用两种类型的UDC技术拍摄的图像。第一种方法在小波分解的卷积神经网络中使用了基于五方有机LED（P-OLED）的显示系统的锥体扩张卷积。第二种方法在基于离散余弦转换的双域网络中采用锥体扩张的卷积，以恢复使用基于透明的有机LED（T-old）UDC系统拍摄的图像。第一种方法产生了非常优质的恢复图像，是欧洲计算机视觉会议（ECCV）2020挑战的胜利条目，该挑战是针对下播放摄像头的图像修复 - 轨道2-基于PSNR和SSIM进行了评估。第二种方法在基于相同指标评估的挑战的轨道1（T-oled）中得分第四。
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.
