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Transform Domain Pyramidal Dilated Convolution Networks for Restoration of Under Display Camera Images

机译:转换域的金字塔膨胀卷积网络恢复显示相机图像的恢复

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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 4th position in Track-1 (T-OLED) of the challenge evaluated based on the same metrics.
机译:显示屏下显示器(UDC)是一种新颖的技术,可以通过提供大的屏幕到体比来使手持设备中的数字成像经验。由于其在显示屏下的定位,UDC图像严重降低。这项工作解决了由于UDC成像而衰退的图像恢复。提出了两个不同的网络,用于恢复用两种类型的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金字塔扩张的卷积,用于基于五分波 - 有机LED(P-OLED)的显示系统。第二种方法采用基于离散余弦变换的双域网络的金字塔扩张卷积,以恢复使用基于透明的UDC系统拍摄的图像。第一种方法产生了非常好的质量恢复的图像,并且是欧洲计算机视觉(ECCV)的获奖条目2020关于基于PSNR和SSIM评估的显示相机轨道2 - P-OLED的图像恢复的挑战。基于相同度量评估的挑战的轨迹-1(T-OLED)中的第二种方法均得分第4位。

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